Tootfinder

Meta-Analysis with JASP, Part II: Bayesian Approaches
Franti\v{s}ek Barto\v{s}, Eric-Jan Wagenmakers
https://arxiv.org/abs/2509.09850 https://arxiv.org/pdf…

Meta-Analysis with JASP, Part II: Bayesian Approaches
Bayesian inference is on the rise, partly because it allows researchers to quantify parameter uncertainty, evaluate evidence for competing hypotheses, incorporate model ambiguity, and seamlessly update knowledge as information accumulates. All of these advantages apply to the meta-analytic settings; however, advanced Bayesian meta-analytic methodology is often restricted to researchers with programming experience. In order to make these tools available to a wider audience, we implemented state-…

Modelling Skewed and Heavy-Tailed Errors in Bayesian Mediation Analysis
Zongyu Li, Mark Steel, Zhiyong Zhang
https://arxiv.org/abs/2508.09311 https://arxiv…

Modelling Skewed and Heavy-Tailed Errors in Bayesian Mediation Analysis
Traditional mediation models in both the frequentist and Bayesian frameworks typically assume normality of the error terms. Violations of this assumption can impair the estimation and hypothesis testing of the mediation effect in conventional approaches. This study addresses the non-normality issue by explicitly modelling skewed and heavy-tailed error terms within the Bayesian mediation framework. Building on the work of Fernandez and Steel (1998), this study introduces a novel family of distri…

Accelerating cosmological inference of interacting dark energy with neural emulators
Karim Carrion
https://arxiv.org/abs/2508.10876 https://arxiv.org/pdf/2…

Accelerating cosmological inference of interacting dark energy with neural emulators
The present thesis aims to tackle two critical aspects of present and future cosmological analysis of Large-Scale Structure (LSS): accurate modelling of the nonlinear matter power spectrum beyond $Λ$CDM, and efficient computational techniques for Bayesian parameter estimation. Both are crucial for testing alternative cosmologies and avoiding spurious results. We focus on the Dark Scattering (DS) model, describing pure momentum transfer between dark matter -- dark energy through the parameter $…

Spin Constraints on 4U 1630-47 via combined Continuum Fitting and Reflection methods: a comparative study using Frequentist and Bayesian statistics
Debtroy Das, Honghui Liu, Zuobin Zhang, Cosimo Bambi, Jiachen Jiang, Johannes Buchner, Andrea Santangelo, Menglei Zhou
https://arxiv.org/abs/2509.09481…

Spin Constraints on 4U 1630-47 via combined Continuum Fitting and Reflection methods: a comparative study using Frequentist and Bayesian statistics
We present a comprehensive Bayesian spectral analysis of the black hole X-ray binary 4U 1630-47 during its 2022 outburst, using simultaneous \textit{NICER} and \textit{NuSTAR} observations. Using the traditional frequentist approach, we build our model combining reflection spectroscopy with continuum fitting techniques and analyse the data. In the Bayesian framework, we jointly constrain the black hole's spin, mass, inclination, and distance within a unified framework. Employing nested sampling…

A Bayesian factor analysis model for non-randomised staggered designs
Constantin Schmidt, Shaun R. Seaman, Beatrice Emmanouil, Leila Reid, Stuart Smith, Daniela De Angelis, Pantelis Samartsidis
https://arxiv.org/abs/2508.09554

A Bayesian factor analysis model for non-randomised staggered designs
The employment of peer supporter workers starting in 2018 was one of the interventions deployed by National Health Service England as part of its Hepatitis C virus (HCV) elimination plan. Peers are individuals with relevant lived experience who educate their communities about the virus and promote testing and treatment. In this paper, we assess the causal effect of the peers intervention on HCV patient case-finding, using data on 22 administrative regions from January 2016 to May 2021. To do th…

The breakdown scale of pionless effective field theory in the three-nucleon sector
Andreas Ekstr\"om, Lucas Platter
https://arxiv.org/abs/2507.08700 h…

The breakdown scale of pionless effective field theory in the three-nucleon sector
We make order-by-order predictions of neutron-deuteron total cross sections up to next-to-next-to-leading order in pionless effective field theory. Using Bayesian methods, we infer a posterior distribution for the breakdown scale. The result shows a mode near 100 MeV, and a combined analysis with neutron-proton scattering further sharpens the inference, placing the mode close to the pion mass scale, consistent with the expected range of pionless effective field theory.

On the computational feasibility of Bayesian end-to-end analysis of LiteBIRD simulations within Cosmoglobe
R. Aurvik, M. Galloway, E. Gjerl{\o}w, U. Fuskeland, A. Basyrov, M. Bortolami, M. Brilenkov, P. Campeti, H. K. Eriksen, L. T. Hergt, D. Herman, M. Monelli, L. Pagano, G. Puglisi, N. Raffuzzi, N. -O. Stutzer, R. M. Sullivan, H. Thommesen, D. J. Watts, I. K. Wehus, D. Adak, E. Allys, A. Anand, J. Aumont, C. Baccigalupi, M. Ballardini, A. J. Banday, R. B. Barreiro, N. Bartolo, S. Bas…

On the computational feasibility of Bayesian end-to-end analysis of LiteBIRD simulations within Cosmoglobe
We assess the computational feasibility of end-to-end Bayesian analysis of the JAXA-led LiteBIRD experiment by analysing simulated time ordered data (TOD) for a subset of detectors through the Cosmoglobe and Commander3 framework. The data volume for the simulated TOD is 1.55 TB, or 470 GB after Huffman compression. From this we estimate a total data volume of 238 TB for the full three year mission, or 70 TB after Huffman compression. We further estimate the running time for one Gibbs sample, fr…

Chaotic Bayesian Inference: Strange Attractors as Risk Models for Black Swan Events
Crystal Rust
https://arxiv.org/abs/2509.08183 https://arxiv.org/pdf/250…

Chaotic Bayesian Inference: Strange Attractors as Risk Models for Black Swan Events
We introduce a new risk modeling framework where chaotic attractors shape the geometry of Bayesian inference. By combining heavy-tailed priors with Lorenz and Rossler dynamics, the models naturally generate volatility clustering, fat tails, and extreme events. We compare two complementary approaches: Model A, which emphasizes geometric stability, and Model B, which highlights rare bursts using Fibonacci diagnostics. Together, they provide a dual perspective for systemic risk analysis, linking B…

Nonparametric Bayesian Inference for Stochastic Reaction-Diffusion Equations
Randolf Altmeyer, Sascha Gaudlitz
https://arxiv.org/abs/2507.06857 https://

Nonparametric Bayesian Inference for Stochastic Reaction-Diffusion Equations
We consider the Bayesian nonparametric estimation of a nonlinear reaction function in a reaction-diffusion stochastic partial differential equation (SPDE). The likelihood is well-defined and tractable by the infinite-dimensional Girsanov theorem, and the posterior distribution is analysed in the growing domain asymptotic. Based on a Gaussian wavelet prior, the contraction of the posterior distribution around the truth at the minimax optimal rate is proved. The analysis of the posterior distribu…

On the True Significance of the Hubble Tension: A Bayesian Error Decomposition Accounting for Information Loss
Nathalia M. N. da Rocha, Andre L. B. Ribeiro, Francisco B. S. Oliveira
https://arxiv.org/abs/2509.09034

On the True Significance of the Hubble Tension: A Bayesian Error Decomposition Accounting for Information Loss
The Hubble tension, a persistent discrepancy between early and late Universe measurements of $H_0$, poses a significant challenge to the standard cosmological model. In this work, we present a new Bayesian hierarchical framework designed to meticulously decompose this observed tension into its constituent parts: standard measurement errors, information loss arising from parameter-space projection, and genuine physical tension. Our approach, employing Fisher matrix analysis with MCMC-estimated l…

Is Repeated Bayesian Interim Analysis Consequence-Free?
Suyu Liu, Beibei Guo, Laura Thompson, Lei Nie, Ying Yuan
https://arxiv.org/abs/2508.07403 https://a…

Is Repeated Bayesian Interim Analysis Consequence-Free?
Interim analyses are commonly used in clinical trials to enable early stopping for efficacy, futility, or safety. While their impact on frequentist operating characteristics is well studied and broadly understood, the effect of repeated Bayesian interim analyses - when conducted without appropriate multiplicity adjustment - remains an area of active debate. In this article, we provide both theoretical justification and numerical evidence illustrating how such analyses affect key inferential pro…

Bayesian Analysis of Non-extensive Parameters in Au-Au Collisions
Randy Dobler, Juliana O. Costa, Marcelo D. Alloy, D\'ebora P. Menezes
https://arxiv.org/abs/2507.01646

Bayesian Analysis of Non-extensive Parameters in Au-Au Collisions
In this work, a Bayesian statistical framework is employed to analyze particle yield ratios in Au-Au collisions, utilizing Non-Extensive Statistics (NES). Through Markov Chain Monte Carlo (MCMC) sampling, we systematically estimate key parameters, including the non-extensive factor $q$, temperature $T$, and chemical potential $μ$. Our analysis confirms previous findings highlighting the suitability and robustness of Bayesian methods in describing heavy-ion collision data. A subsequent Bayes fa…

What's the Buzz About GX 13 1? Constraining Coronal Geometry with QUEEN-BEE: A Bayesian Nested Sampling Framework for X-ray Polarization Rotation Analysis
Swati Ravi, Mason Ng, Herman L. Marshall, Andrea Gnarini
https://arxiv.org/abs/2509.07059

What's the Buzz About GX 13+1? Constraining Coronal Geometry with QUEEN-BEE: A Bayesian Nested Sampling Framework for X-ray Polarization Rotation Analysis
Observations from the Imaging X-ray Polarimetry Explorer (IXPE) have revealed electric vector position angle (EVPA) rotation in several neutron star low-mass X-ray binaries, including the galactic X-ray burster GX 13+1. We developed a novel Bayesian nested sampling framework-"Q-U Event-by-Event Nested sampling for Bayesian EVPA Evolution" (QUEEN-BEE)-to model unbinned Stokes parameters and infer optimal EVPA rotation rates in IXPE data. We then applied this framework to three previous IXPE obse…

Taking the Highway or the Green Road? Conditional Temperature Forecasts Under Alternative SSP Scenarios
Anthoulla Phella, Vasco J. Gabriel, Luis F. Martins
https://arxiv.org/abs/2509.09384

Taking the Highway or the Green Road? Conditional Temperature Forecasts Under Alternative SSP Scenarios
In this paper, using the Bayesian VAR framework suggested by Chan et al. (2025), we produce conditional temperature forecasts up until 2050, by exploiting both equality and inequality constraints on climate drivers like carbon dioxide or methane emissions. Engaging in a counterfactual scenario analysis by imposing a Shared Socioeconomic Pathways (SSPs) scenario of "business as-usual", with no mitigation and high emissions, we observe that conditional and unconditional forecasts would follow a s…

Bayesian Analysis of the $^{86}$Sr$(\alpha, \alpha)$ Reaction to Constrain the $^{86}$Sr$(\alpha, n)$ Cross Section at Astrophysical Energies
Caleb Marshall, Taliah Lansing, David Gribble, Richard Longland, Athanasios Psaltis, Kiana Setoodehnia
https://arxiv.org/abs/2509.05461

Bayesian Analysis of the $^{86}$Sr$(α, α)$ Reaction to Constrain the $^{86}$Sr$(α, n)$ Cross Section at Astrophysical Energies
The Alpha Optical Model Potential (\aomp \!) is a phenomenological approach used to describe elastic scattering where multiple reaction channels are open. It is one of the most critical inputs for the calculation of thermonuclear reaction rates in explosive stellar environments, but uncertainties within the $α$-OMP lead to imprecise predictions hindering comparisons between calculations and observations. In order to improve the precision of the $α$-OMP, additional nuclear physics data are req…

Revisiting the Question of Information Content of EXAFS Spectra through a Bayesian Approach
Lucy Haddad, Diego Gianolio, Andrei Sapelkin
https://arxiv.org/abs/2509.07950 https:/…

Revisiting the Question of Information Content of EXAFS Spectra through a Bayesian Approach
Over the last several decades the Shannon-Nyquist criterion has been widely used as a measure of the maximum information content in EXAFS spectra and provided an upper limit on the number of parameters used in fitting data. However, the criterion implicitly assumes independent parameters which is never the case in EXAFS analysis. Here we introduce a new criterion to measure the information content in EXAFS based on Bayesian approach that lifts the above condition. We test the new criterion by f…

The Milky Way and M31 rotation curves in Yukawa gravity: phenomenology and Bayesian analysis
Davendra S. Hassan, M Dio Danarianto, Anto Sulaksono
https://arxiv.org/abs/2508.04018

The Milky Way and M31 rotation curves in Yukawa gravity: phenomenology and Bayesian analysis
Yukawa gravity provides a generalized framework for modeling gravity modification. We investigate the rotation curve profiles of spiral galaxies under Yukawa-like theories governed by the coupling strength $β$ and the interaction range $λ$. We develop a unified analytical and numerical framework to calculate rotational velocities under Yukawa gravity, which includes contributions from all major galactic components: stellar bulge, disk, dark matter (DM) halo, and central supermassive black hol…

What if we find nothing? Bayesian analysis of the statistical information of null results in future exoplanet habitability and #biosignature surveys: https://arxiv.org/abs/2504.06779 -> interview with the first author: https://www.youtube.com/watch?v=FbnmxiTlBUo

Straightforward Bayesian A/B testing with Dirichlet posteriors
Dustin Hayden, Thomas Armitage
https://arxiv.org/abs/2508.08077 https://arxiv.org/pdf/2508.0…

Straightforward Bayesian A/B testing with Dirichlet posteriors
Bayesian A/B testing investigates metric changes using the joint posterior distribution of two (or more) experimentally-derived datasets. The construction of said joint posterior is often a time-consuming process requiring specialized knowledge and domain expertise. In businesses that perform tens to hundreds of A/B tests per month it is important to have a robust analysis pipeline that can handle the variety of experiments performed on a modern web platform; requiring a domain expert to select…

Carbon-Enhanced Dwarf Stars Are Predominantly a Halo Population
Jay Farihi, Jason L. Sanders, Sophia Lilleengen, Lewis J. Whitehouse, Denis Erkal
https://arxiv.org/abs/2509.06829

Carbon-Enhanced Dwarf Stars Are Predominantly a Halo Population
This paper reports a Galactic kinematical and dynamical analysis of 1003 main-sequence carbon stars. The sample is drawn from the Sloan Digital Sky Survey, and cross-matched with Gaia DR3 to obtain 6-dimensional positions and velocities using a Bayesian framework. The study provides the first reliable distances for a large sample of dwarf carbon stars, which are then analyzed using both space motions and actions. The results are combined with dynamical equilibrium models for the three primary G…

Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributions
Viktor Stein, Wuchen Li
https://arxiv.org/abs/2509.04008 …

Towards understanding Accelerated Stein Variational Gradient Flow -- Analysis of Generalized Bilinear Kernels for Gaussian target distributions
Stein variational gradient descent (SVGD) is a kernel-based and non-parametric particle method for sampling from a target distribution, such as in Bayesian inference and other machine learning tasks. Different from other particle methods, SVGD does not require estimating the score, which is the gradient of the log-density. However, in practice, SVGD can be slow compared to score-estimation-based sampling algorithms. To design a fast and efficient high-dimensional sampling algorithm with the adv…

Testing T2K's Bayesian constraints with priors in alternate parameterisations
Collaboration, Abe, Abe, Akutsu, Alarakia-Charles, Hakim, Monsalve, Anthony, Aoki, Apte, Arai, Arihara, Arimoto, Ashida, Atkin, Babu, Baranov, Barker, Barr, Barrow, Bates, Bathe-Peters, Batkiewicz-Kwasniak, Baudis, Berardi, Berns, Bhattacharjee, Blanchet, Blondel, Boistier, Bolognesi, Bordoni, Boyd, Bronner, Bubak, Avanzini, Caballero, Cadoux, Calabria, Cao, Cap, Carabadjac, Cartwright, Casado, Catanesi, …

Testing T2K's Bayesian constraints with priors in alternate parameterisations
Bayesian analysis results require a choice of prior distribution. In long-baseline neutrino oscillation physics, the usual parameterisation of the mixing matrix induces a prior that privileges certain neutrino mass and flavour state symmetries. Here we study the effect of privileging alternate symmetries on the results of the T2K experiment. We find that constraints on the level of CP violation (as given by the Jarlskog invariant) are robust under the choices of prior considered in the analysis…

Bayesian Analysis of the Neutron Star Equation of State and Model Comparison: Insights from PSR J0437 4715, PSR J0614 3329, and Other Multi-Physics Data
Sk Md Adil Imam, N. K. Patra
https://arxiv.org/abs/2509.07109

Bayesian Analysis of the Neutron Star Equation of State and Model Comparison: Insights from PSR J0437+4715, PSR J0614+3329, and Other Multi-Physics Data
We perform a comprehensive Bayesian analysis to constrain the neutron star (NS) equation of state (EoS) using a wide range of terrestrial and astrophysical data. The terrestrial inputs include quantities related to symmetric nuclear matter (SNM) and symmetry energy up to two times saturation density ($ρ_0\sim$0.16 fm$^{-3}$), derived from finite nuclei and heavy-ion collisions (HICs). The astrophysical constraints incorporate NS radii and tidal deformabilities from recent NICER observations an…

Optimal Experimental Design for Microplastics Sampling Experiments
Marco A. Aquino-L\'opez, Ana Carolina Ruiz-Fern\'andez, Joan-Albert Sanchez-Cabeza, J. Andr\'es Christen
https://arxiv.org/abs/2507.08170

Optimal Experimental Design for Microplastics Sampling Experiments
Microplastics contamination is one of the most rapidly growing research topics. However, monitoring microplastics contamination in the environment presents both logistical and statistical challenges, particularly when constrained resources limit the scale of sampling and laboratory analysis. In this paper, we propose a Bayesian framework for the optimal experimental design of microplastic sampling campaigns. Our approach integrates prior knowledge and uncertainty quantification to guide decisio…

Real-time Terrain Analysis for Off-road Autonomous Vehicles
Edwina Lewis, Aditya Parameshwaran, Laura Redmond, Yue Wang
https://arxiv.org/abs/2506.21347 ht…

Real-time Terrain Analysis for Off-road Autonomous Vehicles
This research addresses critical autonomous vehicle control challenges arising from road roughness variation, which induces course deviations and potential loss of road contact during steering operations. We present a novel real-time road roughness estimation system employing Bayesian calibration methodology that processes axle accelerations to predict terrain roughness with quantifiable confidence measures. The technical framework integrates a Gaussian process surrogate model with a simulated …

Survival Analysis with Discrete Biomarkers Under a Semiparametric Bayesian Conditional Poisson Model
Aijun Yang, Phineas T. Hamilton, Brad H. Nelson, Julian J. Lum, Mary Lesperance, Farouk S. Nathoo
https://arxiv.org/abs/2509.08162

Survival Analysis with Discrete Biomarkers Under a Semiparametric Bayesian Conditional Poisson Model
Discrete biomarkers derived as cell densities or counts from tissue microarrays and immunostaining are widely used to study immune signatures in relation to survival outcomes in cancer. Although routinely collected, these signatures are not measured with exact precision because the sampling mechanism involves examination of small tissue cores from a larger section of interest. We model these error-prone biomarkers as Poisson processes with latent rates, inducing heteroscedasticity in their cond…

Bayesian Non-Negative Matrix Factorization with Correlated Mutation Type Probabilities for Mutational Signatures
Iris Lang, Jenna Landy, Giovanni Parmigiani
https://arxiv.org/abs/2506.15855

Bayesian Non-Negative Matrix Factorization with Correlated Mutation Type Probabilities for Mutational Signatures
Somatic mutations, or alterations in DNA of a somatic cell, are key markers of cancer. In recent years, mutational signature analysis has become a prominent field of study within cancer research, commonly with Nonnegative Matrix Factorization (NMF) and Bayesian NMF. However, current methods assume independence across mutation types in the signatures matrix. This paper expands upon current Bayesian NMF methodologies by proposing novel methods that account for the dependencies between the mutatio…

Code Difference Guided Fuzzing for FPGA Logic Synthesis Compilers via Bayesian Optimization
Zhihao Xu, Shikai Guo, Guilin Zhao, Peiyu Zou, Siwen Wang, Qian Ma, Hui Li, Furui Zhan
https://arxiv.org/abs/2508.17713

Code Difference Guided Fuzzing for FPGA Logic Synthesis Compilers via Bayesian Optimization
Field Programmable Gate Arrays (FPGAs) play a crucial role in Electronic Design Automation (EDA) applications, which have been widely used in safety-critical environments, including aerospace, chip manufacturing, and medical devices. A critical step in FPGA development is logic synthesis, which enables developers to translate their software designs into hardware net lists, which facilitates the physical implementation of the chip, detailed timing and power analysis, gate-level simulation, test …

Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
Kyeongwon Lee, Lizhen Lin, Jaewoo Park, Seonghyun Jeong
https://arxiv.org/abs/2506.19144

Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
This work establishes that sparse Bayesian neural networks achieve optimal posterior contraction rates over anisotropic Besov spaces and their hierarchical compositions. These structures reflect the intrinsic dimensionality of the underlying function, thereby mitigating the curse of dimensionality. Our analysis shows that Bayesian neural networks equipped with either sparse or continuous shrinkage priors attain the optimal rates which are dependent on the intrinsic dimension of the true structu…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators
Ponkrshnan Thiagarajan, Tamer A. Zaki, Michael D. Shields

Detection of a Higher Harmonic Quasi-normal Mode in the Ringdown Signal of GW231123
Hai-Tian Wang, Shao-Peng Tang, Peng-Cheng Li, Yi-Zhong Fan
https://arxiv.org/abs/2509.02047 h…

Detection of a Higher Harmonic Quasi-normal Mode in the Ringdown Signal of GW231123
The ringdown phase of a gravitational wave signal from a binary black hole merger offers a unique laboratory for testing general relativity in the strong-field regime and probing the properties of the final remnant black hole. In this study, we analyze the ringdown of GW231123 and find strong evidence for a multimode quasinormal spectrum. Our analysis employs two time-domain methodologies: a full Bayesian inference and an enhanced F-statistic framework, which we extend to enable the calculation…

Bayesian Analysis of Wave-Optics Gravitationally Lensed Massive Black Hole Binaries with Space-Based Gravitational Wave Detector
Yong Yuan, Minghui Du, Xin-yi Lin, Peng Xu, Xilong Fan
https://arxiv.org/abs/2509.01888

Bayesian Analysis of Wave-Optics Gravitationally Lensed Massive Black Hole Binaries with Space-Based Gravitational Wave Detector
Within a Bayesian statistical framework, we jointly estimate the source and lens parameters and evaluate the relative evidence between the lensed and unlensed models. This work focuses on the wave optics effects induced by a point mass (PM) lens on gravitational waves (GW) from equal-mass massive binary black holes (MBHB), and assesses the capability of the space-based GW detector Taiji to detect such effects. Specifically, we investigate the impact of the redshifted lens mass MLz in the range …

Posterior contraction rates of computational methods for Bayesian data assimilation
Erik Burman, Mingfei Lu
https://arxiv.org/abs/2506.14685 https://

Posterior contraction rates of computational methods for Bayesian data assimilation
In this paper, we analyze posterior consistency of a Bayesian data assimilation problem under discretization. We prove convergence rates for the discrete posterior to ground truth solution under both conforming discretization and finite element discretization (usually non-conforming). The analysis is based on the coupling of asymptotics between the number of samples and the dimension of discrete spaces. In the finite element discretization, tailor-made discrete priors, instead of the discretiza…

Global Bayesian Analysis of $\mathrm{J}/\psi$ Photoproduction on Proton and Lead Targets
Heikki M\"antysaari, Hendrik Roch, Farid Salazar, Bj\"orn Schenke, Chun Shen, Wenbin Zhao
https://arxiv.org/abs/2507.14087

Global Bayesian Analysis of $\mathrm{J}/ψ$ Photoproduction on Proton and Lead Targets
We perform a global Bayesian analysis of diffractive $\mathrm{J}/ψ$ production in $γ+p$ and $γ+\mathrm{Pb}$ collisions using a color glass condensate (CGC) based calculation framework. As past calculations have shown that CGC-based models typically overpredict the $\mathrm{J}/ψ$ production in $γ+\mathrm{Pb}$ collisions at high center of mass energy, we address the question of whether it is possible to describe coherent and incoherent diffractive $\mathrm{J}/ψ$ data from $γ+p$ collisions …

A Bayesian approach to time-domain Photonic Doppler Velocimetry
J. R. Allison (First Light Fusion Ltd), R. Bordas (First Light Fusion Ltd), J. Read (First Light Fusion Ltd), G. Burdiak (First Light Fusion Ltd), V. Beltr\'an (First Light Fusion Ltd), N. Joiner (First Light Fusion Ltd), H. Doyle (First Light Fusion Ltd), N. Hawker (First Light Fusion Ltd), J. Skidmore (First Light Fusion Ltd), T. Ao (Sandia National Laboratories), A. Porwitzky (Sandia National Laboratories), D. Dolan…

A Bayesian approach to time-domain Photonic Doppler Velocimetry
Photonic Doppler Velocimetry (PDV) is an established technique for measuring the velocities of fast-moving surfaces in high-energy-density experiments. In the standard approach to PDV analysis, a short-time Fourier transform (STFT) is used to generate a spectrogram from which the velocity history of the target is inferred. The user chooses the form, duration and separation of the window function. Here we present a Bayesian approach to infer the velocity directly from the PDV oscilloscope trace,…

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Mohammed Elmusrati
https://arxiv.org/abs/2508.15719 https://

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI
Extracting meaning from uncertain, noisy data is a fundamental problem across time series analysis, pattern recognition, and language modeling. This survey presents a unified mathematical framework that connects classical estimation theory, statistical inference, and modern machine learning, including deep learning and large language models. By analyzing how techniques such as maximum likelihood estimation, Bayesian inference, and attention mechanisms address uncertainty, the paper illustrates …

Bayesian Bootstrap-based Gaussian Copula Model for Mixed Data with High Missing Rates
Seongmin Kim, Jeunghun Oh, Hungkuk Ko, Jeongmin Park, Jaeyong Lee
https://arxiv.org/abs/2507.06785

Bayesian Bootstrap-based Gaussian Copula Model for Mixed Data with High Missing Rates
Missing data is a common issue in various fields such as medicine, social sciences, and natural sciences, and it poses significant challenges for accurate statistical analysis. Although numerous imputation methods have been proposed to address this issue, many of them fail to adequately capture the complex dependency structure among variables. To overcome this limitation, models based on the Gaussian copula framework have been introduced. However, most existing copula-based approaches do not ac…

Bayesian Analysis and Analytic Continuation of Scattering Amplitudes from Lattice QCD
Miguel Salg, Fernando Romero-L\'opez, William I. Jay
https://arxiv.org/abs/2506.16161

Bayesian Analysis and Analytic Continuation of Scattering Amplitudes from Lattice QCD
We present a novel procedure for analyzing the lattice-QCD spectrum via the finite-volume formalism to obtain constraints on multi-hadron scattering amplitudes at both real and complex energies. This approach combines a Bayesian reconstruction of the scattering amplitude on the real axis with Nevanlinna interpolation for analytic continuation to complex-valued energies. The method is non-parametric, inherently accounting for parametrization dependence within the uncertainty. We demonstrate the …

Investigating the Influence of Asymmetric Errors on Retrievals of Exoplanet Transmission Spectra
Jack J. Davey, Kai Hou Yip, Quentin Changeat, Ingo P. Waldmann
https://arxiv.org/abs/2507.19223

Investigating the Influence of Asymmetric Errors on Retrievals of Exoplanet Transmission Spectra
In studies of exoplanet atmospheres using transmission spectroscopy, Bayesian retrievals are the most popular form of analysis. In these procedures it is common to adopt a Gaussian likelihood. However, this implicitly assumes that the upper and lower error bars on the spectral points are equal. With recent observations from the James Webb Space Telescope (JWST) offering higher quality of data, it is worth revisiting this assumption to understand the impact that an asymmetry between the error ba…

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning
Liliang Wang, Alex A. Gorodetsky
https://arxiv.org/abs/2507.20379

A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning
We establish a general, non-asymptotic error analysis framework for understanding the effects of incremental approximations made by practical approaches for Bayesian sequential learning (BSL) on their long-term inference performance. Our setting covers inverse problems, state estimation, and parameter-state estimation. In these settings, we bound the difference-termed the learning error-between the unknown true posterior and the approximate posterior computed by these approaches, using three wi…

Efficient mechanical evaluation of railway earthworks using a towed seismic array and Bayesian inference of MASW data
Audrey Burzawa, Ludovic Bodet, Marine Dangeard, Brian Barrett, Daniel Byrne, Robert Whitehead, Corentin Chaptal, Jos\'e Cunha Teixeira, Julio Cardenas, Ramon Sanchez Gonzalez, Asger Eriksen, Amine Dhemaied
https://

Efficient mechanical evaluation of railway earthworks using a towed seismic array and Bayesian inference of MASW data
Assessing Railway Earthworks (RE) requires non-destructive and time-efficient diagnostic tools. This study evaluates the relevance of shear-wave velocity ($V_s$) profiling using Multichannel Analysis of Surface Waves (MASW) for detecting Low Velocity Layers (LVLs) in disturbed RE zones. To enhance time-efficiency, a towed seismic setup (Landstreamer) was compared with a conventional one. Once qualified, the Landstreamer was deployed on the ballast for roll-along acquisition, showing greatly imp…

Bayesian Estimation and Regularization Techniques in Categorical Data Analysis
Jan Kalina
https://arxiv.org/abs/2509.02222 https://arxiv.org/pdf/2509.02222…

Bayesian Estimation and Regularization Techniques in Categorical Data Analysis
This paper explores Bayesian estimation for categorical data, focusing on simple yet effective models that provide a foundation for applying more advanced methods accurately and reliably in real-world applications. We begin by revisiting Bayesian estimators for the binomial distribution and investigating their properties. Next, we develop hypothesis tests for categorical data (sign test, homogeneity test, symmetry test) based on regularized maximum likelihood estimates of the probabilities. Fin…

Understanding uncertainty in Bayesian cluster analysis
Cecilia Balocchi, Sara Wade
https://arxiv.org/abs/2506.16295 https://arxiv.org…

Understanding uncertainty in Bayesian cluster analysis
The Bayesian approach to clustering is often appreciated for its ability to provide uncertainty in the partition structure. However, summarizing the posterior distribution over the clustering structure can be challenging, due the discrete, unordered nature and massive dimension of the space. While recent advancements provide a single clustering estimate to represent the posterior, this ignores uncertainty and may even be unrepresentative in instances where the posterior is multimodal. To enhanc…

Active Digital Twins via Active Inference
Matteo Torzoni, Domenico Maisto, Andrea Manzoni, Francesco Donnarumma, Giovanni Pezzulo, Alberto Corigliano
https://arxiv.org/abs/2506.14453

Active Digital Twins via Active Inference
Digital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes. However, conventional digital twins rely primarily on passive data assimilation, which limits their adaptability in uncertain and dynamic environments. This paper introduces the active digital twin paradigm, based on active inference. Active inference is a neuroscience-inspired, Bayesian framework for probabilistic reasoning and…

Determination of $|V_{\rm cb}|$ using Bayesian analysis of the $B \rightarrow D^{*}\ell {\bar \nu}_\ell$ semileptonic decay width with four-loop QCD corrections
Wang Li, Xing-Gang Wu, Hua Zhou, Xu-Chang Zheng
https://arxiv.org/abs/2506.18026

Determination of $|V_{\rm cb}|$ using Bayesian analysis of the $B \rightarrow D^{*}\ell {\bar ν}_\ell$ semileptonic decay width with four-loop QCD corrections
The Cabibbo-Kobayashi-Maskawa matrix element $|V_{\rm cb}|$ is an important Standard Model parameter, whose value can be determined by using the semi-leptonic decay $B\rightarrow D^{*}\ell{\bar ν}_\ell$. The perturbative QCD (pQCD) corrections to the $B \to D^{*}$ transform form factor ${\cal F}(w)$ has been known up to the N$^3$LO level, whose magnitude remains sensitive to the choice of renormalization scale $μ_r$. To improve the precision of ${\cal F}(w)$ and hence $|V_{\rm …

A nonparametric Bayesian analysis of independent and identically distributed observations of covariate-driven Poisson processes
Patric Dolmeta, Matteo Giordano
https://arxiv.org/abs/2509.02299

A nonparametric Bayesian analysis of independent and identically distributed observations of covariate-driven Poisson processes
An important task in the statistical analysis of inhomogeneous point processes is to investigate the influence of a set of covariates on the point-generating mechanism. In this article, we consider the nonparametric Bayesian approach to this problem, assuming that $n$ independent and identically distributed realizations of the point pattern and the covariate random field are available. In many applications, different covariates are often vastly diverse in physical nature, resulting in anisotrop…

Bayesian analysis of properties of nuclear matter with the FOPI experimental data
Guojun Wei, Manzi Nan, Pengcheng Li, Yongjia Wang, Qingfeng Li, Gaochan Yong, Fuhu Liu
https://arxiv.org/abs/2509.03406

Bayesian analysis of properties of nuclear matter with the FOPI experimental data
Based on the ultra-relativistic quantum molecular dynamics (UrQMD) transport model, combined with experimental data of directed flow, elliptic flow, and nuclear stopping power measured by FOPI in $\rm ^{197}Au+^{197}Au$ collisions at beam energies ($E_{lab}$) of 0.25 and 0.4 GeV/nucleon, the incompressibility of the nuclear equation of state $K_0$, the nucleon effective mass $m^*$, and the in-medium correction factor ($F$, with respect to free-space values) on the nucleon-nucleon elastic cross …

From new physics to a running power law and back again: Minimal refitting techniques for the reconstruction of the gravitational-wave background signal in pulsar timing array data
David Esmyol, Antonio J. Iovino, Kai Schmitz
https://arxiv.org/abs/2506.23574

From new physics to a running power law and back again: Minimal refitting techniques for the reconstruction of the gravitational-wave background signal in pulsar timing array data
Pulsar timing array (PTA) collaborations recently presented evidence for a gravitational-wave background (GWB) signal at nanohertz frequencies. In this paper, we introduce new refitting techniques for PTA data analysis that elevate related techniques in the literature to a more rigorous level and thus provide the basis for fast and accurate Bayesian inference and physically intuitive model comparisons. The key idea behind our approach is to construct maps Φfrom GWB spectral models to a running…

Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
Conor Rosato, Joshua Murphy, Simon Maskell, John Harris
https://arxiv.org/abs/2506.14536

Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
The introduction of the rotavirus vaccine in the United Kingdom (UK) in 2013 led to a noticeable decline in laboratory reports in subsequent years. To assess the impact of vaccination on rotavirus transmissibility we calibrated a stochastic compartmental epidemiological model using Sequential Monte Carlo (SMC) methods. Our analysis focuses on estimating the time-varying transmissibility parameter and documenting its evolution before and after vaccine rollout. We observe distinct periods of incr…

Stellar Dynamics in Open Clusters Increases the Binary Fraction and Mass Ratios: Evidence from Photometric Binaries in 35 Open Clusters
Anna C. Childs, Aaron M. Geller
https://arxiv.org/abs/2506.20889

Stellar Dynamics in Open Clusters Increases the Binary Fraction and Mass Ratios: Evidence from Photometric Binaries in 35 Open Clusters
Using the Bayesian Analysis of Stellar Evolution-9 (BASE-9) code and Gaia DR3, Pan-STARRS, and 2MASS data, we identify photometric binaries in 35 open clusters (OCs) and constrain their masses. We find a strong correlation between the binary fraction and cluster dynamical age and an even stronger correlation between core binary fraction and cluster dynamical age. We find the binary mass-ratio (q) distribution of dynamically young OCs is statistically distinct from that of the old OCs. On averag…

Hierarchical Maximum Likelihood Estimation for Time-Resolved NMR Data
Lennart H. Bosch, Pernille R. Jensen, Nico Striegler, Thomas Unden, Jochen Scharpf, Usman Qureshi, Philipp Neumann, Martin Gierse, John W. Blanchard, Stephan Knecht, Jochen Scheuer, Ilai Schwart, Martin B. Plenio
https://arxiv.org/abs/2508.14902

Hierarchical Maximum Likelihood Estimation for Time-Resolved NMR Data
Metabolic monitoring and reaction rate estimation using hyperpolarized NMR technology requires accurate quantitative analysis of multidimensional data scenarios. Currently, this analysis is often performed in a two-stage procedure, which is prone to errors in uncertainty propagation and estimation. We propose an approach derived from a Bayesian hierarchical model that intrinsically propagates uncertainties and operates on the full data to maximize the precision at minimal uncertainty. In an ana…

A Minimalist Physics-Informed Model for Predicting Extreme Conflict Fatalities
Yair Neuman, Yochai Cohen
https://arxiv.org/abs/2507.11576 https://

A Minimalist Physics-Informed Model for Predicting Extreme Conflict Fatalities
The complexity of armed conflicts is expressed in the number of fatalities that may span several orders of magnitude. This study presents a minimalist, physics-informed approach to estimating the likelihood of extreme conflict fatalities at the country level of analysis using Bayesian modeling and energy-based dynamics. Leveraging the Boltzmann distribution to construct a Dirichlet prior, we formulate a predictive measure that captures the underlying entropy and energy states of conflict severi…

Bayesian and frequentist perspectives agree on dynamical dark energy
Laura Herold, Tanvi Karwal
https://arxiv.org/abs/2506.12004 https://

Bayesian and frequentist perspectives agree on dynamical dark energy
Baryon acoustic oscillation data from the Dark Energy Spectroscopic Instrument (DESI) show evidence of a deviation from a cosmological constant $Λ$ within a Bayesian analysis. In this work, we validate that frequentist constraints from profile likelihoods on the Chevallier-Polarski-Linder parameters $w_0$, $w_a$ are in excellent agreement with the Bayesian constraints when combining with Planck cosmic microwave background, Planck and Atacama Cosmology Telescope lensing, and either Pantheon+ or…

Efficient Bayesian analysis of kilonovae and GRB afterglows with fiesta
Hauke Koehn, Thibeau Wouters, Peter T. H. Pang, Mattia Bulla, Henrik Rose, Hannah Wichern, Tim Dietrich
https://arxiv.org/abs/2507.13807

Efficient Bayesian analysis of kilonovae and GRB afterglows with fiesta
Gamma-ray burst (GRB) afterglows and kilonovae (KNe) are electromagnetic transients that can accompany binary neutron star (BNS) mergers. Therefore, studying their emission processes is of general interest for constraining cosmological parameters or the behavior of ultra-dense matter. One common method to analyze electromagnetic data from BNS mergers is to sample a Bayesian posterior over the parameters of a physical model for the transient. However, sampling the posterior is computationally co…

CONAN: A Python package for modeling lightcurve and radial velocity data of exoplanetary systems
Babatunde Akinsanmi, Monika Lendl, Andreas Krenn
https://arxiv.org/abs/2508.20196

CONAN: A Python package for modeling lightcurve and radial velocity data of exoplanetary systems
We present CONAN (COde for exoplaNet ANalysis), an open-source Python package for comprehensive analyses of exoplanetary systems. It provides a unified Bayesian framework to simultaneously analyze diverse exoplanet datasets to derive global system parameters. CONAN allows to consistently model photometric transit light curves, occultations, phase curves, and radial velocity measurements, while detrending each dataset with any combination of parametric, sinusoidal, Gaussian Processes, and spline…

Bayesimax Theory: Selecting Priors by Minimizing Total Information
Sitaram Vangala
https://arxiv.org/abs/2508.15976 https://arxiv.org/pdf/2508.15976…

Bayesimax Theory: Selecting Priors by Minimizing Total Information
We introduce Bayesimax theory, a paradigm for objective Bayesian analysis which selects priors by applying minimax theory to prior disclosure games. In these games, the uniquely optimal strategy for a Bayesian agent upon observing the data is to reveal their prior. As such, the prior chosen by minimax theory is, in effect, the implicit prior of minimax agents. Since minimax analysis disregards prior information, this prior is arguably noninformative. We refer to minimax solutions of certain pri…

Bayesian analysis of the causal reference-based model for missing data in clinical trials
Brendah Nansereko, Marcel Wolbers, James Carpenter
https://arxiv.org/abs/2507.00680

Bayesian analysis of the causal reference-based model for missing data in clinical trials
The statistical analysis of clinical trials is often complicated by missing data. Patients sometimes experience intercurrent events (ICEs), which usually (although not always) lead to missing subsequent outcome measurements for such individuals. The reference-based imputation methods were proposed by Carpenter et al. (2013) and have been commonly adopted for handling missing data due to ICEs when estimating treatment policy strategy estimands. Conventionally, the variance for reference-based es…

Destruction of "Peas in a Pod?" A Candidate Multi-planet System Around the Nearby, Bright Star, HD208487
Rafael I. Rubenstein, James S. Jenkins, Pablo A. Pe\~na R., Carolina Charalambous, Mikko Tuomi, Douglas R. Alves, Jos\'e Vines, Mat\'ias R. D\'iaz, Suman Saha, Paul R. Butler, Jeffrey D. Crane, Steve Shectman, Johanna K. Teske, David Osip, Zahra Essack, Benjamin T. Montet, Adina D. Feinstein, Cristobal Petrovich

Destruction of "Peas in a Pod?" A Candidate Multi-planet System Around the Nearby, Bright Star, HD208487
We re-investigate the HD208487 system to test the reality of the proposed HD208487c world. We also search for additional companions using applied Bayesian statistics and 15+ years of new RV data from the HARPS and the PFS instruments that were taken post-discovery of HD208487b. The RV data was analyzed with GLS Periodograms, followed by Bayesian analysis using the EMPEROR code. We scrutinised various stellar activity indices to search for any corresponding peaks in the power spectra, correlat…

Bayesian Regression Analysis with the Drift-Diffusion Model
Zekai Jin (Mental Health Data Science, New York State Psychiatric Institute, New York, USA), Yaakov Stern (Departments of Neurology, Columbia University Irving Medical Center, New York, USA), Seonjoo Lee (Mental Health Data Science, New York State Psychiatric Institute, New York, USA, Department of Biostatistics, Columbia University Irving Medical Center, New York, USA)

Bayesian Regression Analysis with the Drift-Diffusion Model
The Drift-Diffusion Model (DDM) is widely used in neuropsychological studies to understand the decision process by incorporating both reaction times and subjects' responses. Various models have been developed to estimate DDM parameters, with some employing Bayesian inference. However, when examining associations between phenotypes of interest and DDM parameters, most studies adopt a two-step approach: first estimating DDM parameters, then applying a separate statistical model to the estimated v…

Beyond Extreme Burstiness: Evolving Star Formation Efficiency as the Key to Early Galaxy Abundance
Abhijnan Kar, Shadab Alam, Joseph Silk
https://arxiv.org/abs/2507.20606 https:…

Beyond Extreme Burstiness: Evolving Star Formation Efficiency as the Key to Early Galaxy Abundance
JWST observations have revealed an overabundance of bright galaxies at $z \geq 9$, creating apparent tensions with theoretical predictions within standard $Λ$CDM cosmology. We address this challenge using a semi-empirical approach that connects dark matter halos to observed UV luminosity through physically motivated double power-law star formation efficiency (SFE) model as a function of halo mass, redshift and perform joint Bayesian analysis of luminosity functions spanning $z = 4 - 16$ using …

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Timothy Disher, Chris Cameron, Brian Hutton
https://arxiv.org/abs/2506.22154

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Network meta-analysis (NMA) synthesizes evidence for multiple treatments, but decisions on node formation can have important statistical implications including bias or inflated uncertainty. Existing data-driven methods often lack flexibility or fail to fully account for node uncertainty and adjust for between-trial heterogeneity simultaneously. We introduce a Bayesian non-parametric framework using a Dirichlet process prior with a regularized horseshoe base measure. This data-driven approach al…

Nuclear suppression in diffractive vector meson production within the color glass condensate framework
Heikki M\"antysaari, Hendrik Roch, Farid Salazar, Bj\"orn Schenke, Chun Shen, Wenbin Zhao
https://arxiv.org/abs/2508.21562

Nuclear suppression in diffractive vector meson production within the color glass condensate framework
We perform a global Bayesian analysis of diffractive $\mathrm{J}/ψ$ production in $γ+p$ and $γ+\mathrm{Pb}$ collisions within a Color Glass Condensate based framework. Using data from HERA and the LHC, we find that a simultaneous description of $γ+p$ and $γ+\mathrm{Pb}$ observables is challenging. Introducing a global $K$-factor to account for theoretical uncertainties improves the agreement with data and enhances the framework's predictive power. We present predictions for integrated $\ma…

Simulation-based Inference via Langevin Dynamics with Score Matching
Haoyu Jiang, Yuexi Wang, Yun Yang
https://arxiv.org/abs/2509.03853 https://arxiv.org/p…

Simulation-based Inference via Langevin Dynamics with Score Matching
Simulation-based inference (SBI) enables Bayesian analysis when the likelihood is intractable but model simulations are available. Recent advances in statistics and machine learning, including Approximate Bayesian Computation and deep generative models, have expanded the applicability of SBI, yet these methods often face challenges in moderate to high-dimensional parameter spaces. Motivated by the success of gradient-based Monte Carlo methods in Bayesian sampling, we propose a novel SBI method …

Inferring the Merger History of Primordial Black Holes from Gravitational-Wave data and the Stochastic Signatures
Sourav Roy Chowdhury, Aritra Basak, Maxim Khlopov, Maxim Krasnov
https://arxiv.org/abs/2507.21332

Inferring the Merger History of Primordial Black Holes from Gravitational-Wave data and the Stochastic Signatures
Primordial black holes (PBHs) are well-motivated candidates for cold dark matter and may also account for a fraction of the binary black hole mergers observed by the LIGO-Virgo-KAGRA Collaboration. In this study, we investigate the gravitational-wave signatures of PBHs, with a particular focus on evaluating their integrated contribution to the stochastic gravitational-wave background arising from binary mergers over a broad range of redshifts. We perform a Bayesian analysis of gravitational-wav…

Bayesian Multivariate Sparse Functional PCA
Joseph Sartini, Scott Zeger, Ciprian Crainiceanu
https://arxiv.org/abs/2509.03512 https://arxiv.org/pdf/2509.03…

Bayesian Multivariate Sparse Functional PCA
Functional Principal Components Analysis (FPCA) provides a parsimonious, semi-parametric model for multivariate, sparsely-observed functional data. Frequentist FPCA approaches estimate principal components (PCs) from the data, then condition on these estimates in subsequent analyses. As an alternative, we propose a fully Bayesian inferential framework for multivariate, sparse functional data (MSFAST) which explicitly models the PCs and incorporates their uncertainty. MSFAST builds upon the FAST…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Bayesian estimation of coupling strength and heterogeneity in a coupled oscillator model from mac...
Yusuke Kato, Shuhei Kashiwamura, Emiri Watanabe, Masato Okada,…

Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
Dejiang Li, Tonghua Liu, Ao Liu, Cuihong Wen, Jieci Wang, Kai Liao, Jiaxing Cui
https://arxiv.org/abs/2508.19311

Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
With the advancement of third-generation gravitational wave detectors, the identification of strongly lensed gravitational wave (GW) events is expected to play an increasingly vital role in cosmology and fundamental physics. However, traditional Bayesian inference methods suffer from combinatorial computational overhead as the number of events grows, making real-time analysis infeasible. To address this, we propose a deep learning model named Squeeze-and-Excitation Multilayer Perceptron Data-ef…

How Holographic is the Dark Energy? A Spline Nodal reconstruction approach
Miguel A. Zapata, Gabriela Garcia-Arroyo, Shahnawaz A. Adil, J. Alberto Vazquez
https://arxiv.org/abs/2507.22292

How Holographic is the Dark Energy? A Spline Nodal reconstruction approach
In this work, we explore the generalized holographic dark energy (HDE) scenario. We relate the HDE density to the future-horizon scale via a function $f(a)$, which we reconstruct via spline-based nodal interpolation. We perform a Bayesian analysis to assess model's consistency with current observations, including baryon acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI) DR1, Type Ia supernovae (SNe Ia) from the Union3 and Pantheon+ compilations, and local measureme…

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Large sample scaling analysis of the Zig-Zag algorithm for Bayesian inference
Sanket Agrawal, Joris Bierkens, Gareth O. Roberts

Bayesian Modeling of Long-Term Dynamics in Indian Temperature Extremes
Chitradipa Chakraborty
https://arxiv.org/abs/2507.01540 https://

Bayesian Modeling of Long-Term Dynamics in Indian Temperature Extremes
Annual maximum temperature data provides crucial insights into the impacts of climate change, especially for regions like India, where temperature variations have significant implications for agriculture, health, and infrastructure. In this study, we propose the Coupled Continuous Time Random Walk (CTRW) model to analyze annual maximum temperature data in India from 1901 to 2017 and compare its performance with the Bayesian Spectral Analysis Regression (BSAR) model. The CTRW model extends the s…

Probing Picohertz Gravitational Waves with Pulsars
Qinyuan Zheng, Chiara M. F. Mingarelli, William DeRocco, Jonathan Nay, Kimberly K. Boddy, Jeff A. Dror
https://arxiv.org/abs/2508.21582

Probing Picohertz Gravitational Waves with Pulsars
With periods much longer than the duration of current pulsar timing surveys, gravitational waves in the picohertz (pHz) regime are not detectable in the typical analysis framework for pulsar timing data. However, signatures of these low-frequency signals persist in the slow variation of pulsar timing parameters. In this work, we present the results of the first Bayesian search for continuous pHz gravitational waves using the drift of two sensitive pulsar timing parameters -- time derivative of …

Outlier-robust Bayesian Multivariate Analysis with Correlation-intact Sandwich Mixture
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
https://arxiv.org/abs/2508.18004 https://

Outlier-robust Bayesian Multivariate Analysis with Correlation-intact Sandwich Mixture
Handling outliers is a fundamental challenge in multivariate data analysis, as outliers may distort structures of correlation or conditional independence. Although robust Bayesian inference has been extensively studied for univariate settings, theoretical results ensuring posterior robustness in multivariate models are scarce. We propose a novel scale mixture of multivariate normals called correlation-intact sandwich mixture, where the scale parameters are real-valued and follow the unfolded lo…

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis
Bo Liang, He Wang
https://arxiv.org/abs/2507.11192 https://

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis
The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy, emphasizing the need for rapid and detailed parameter estimation and population-level analyses. Traditional Bayesian inference methods, particularly Markov chain Monte Carlo, face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data. This review examines the em…

Evaluating the effect of different non-informative prior specifications on the Bayesian proportional odds model in randomised controlled trials: a simulation study
Chris J. Selman, Katherine J. Lee, Michael Dymock, Ian C. Marschner, Steven Y. C. Tong, Mark Jones, Robert K. Mahar
https://arxiv.org/abs/2507.21491

Evaluating the effect of different non-informative prior specifications on the Bayesian proportional odds model in randomised controlled trials: a simulation study
Background: Ordinal outcomes combine multiple distinct ordered patient states into a single endpoint and are commonly analysed using proportional odds (PO) models in clinical trials. When using a Bayesian approach, it is not obvious what the influence of a 'non-informative' prior is in the analysis of a fixed design or on early stopping decisions in adaptive designs. Methods: This study compares different non-informative prior specifications for the Bayesian PO model in the context of both a …

Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging
Matteo Guardiani, Vincent Eberle, Margret Westerkamp, Julian R\"ustig, Philipp Frank, Torsten En{\ss}lin
https://arxiv.org/abs/2506.20758

Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging
Modern observatories are designed to deliver increasingly detailed views of astrophysical signals. To fully realize the potential of these observations, principled data-analysis methods are required to effectively separate and reconstruct the underlying astrophysical components from data corrupted by noise and instrumental effects. In this work, we introduce a novel multi-frequency Bayesian model of the sky emission field that leverages latent-space tension as an indicator of model misspecifica…

Updated Astrophysical Equation-of-State Constraints on the Color-Superconducting Gap
Aleksi Kurkela, Krishna Rajagopal, Rachel Steinhorst
https://arxiv.org/abs/2508.20763 https:…

Updated Astrophysical Equation-of-State Constraints on the Color-Superconducting Gap
We summarize and update using new NICER measurements the results of arXiv:2401.16253, in which we used various astrophysical neutron-star observations to set an upper bound on the CFL color-superconducting gap in a range of baryon chemical potentials $μ_B \in [2.1,3.2]$, above those reached within neutron stars. We also corroborate the ``reasonable" constraint from arXiv:2401.16253 on the maximum value of the color-superconducting gap by performing a new Bayesian analysis using a prior that ex…

Constraints on Logarithmic Model Extensions of Symmetric Teleparallel Gravity
Purnendu Karmakar, Sandeep Haridasu, Atsushi Nishizawa
https://arxiv.org/abs/2508.21054 https://

Constraints on Logarithmic Model Extensions of Symmetric Teleparallel Gravity
We address various cosmological phenomenologies in the symmetric teleparallel framework both in background and perturbation such as cosmic expansion, gravitational coupling constant, gravitational waves propagation. Focusing on logarithmic extensions of $f(Q)$ models, we performed Bayesian analysis using the most-recent cosmological data, DESI DR2, Pantheon+. We also utilized a compilation of redshift space distortions ($f σ_8$) dataset to constrain the growth of structures in each of the mode…

An interpretable family of projected normal distributions and a related copula model for Bayesian analysis of hypertoroidal data
Shogo Kato, Gianluca Mastrantonio, Masayuki Ishikawa
https://arxiv.org/abs/2508.16432

An interpretable family of projected normal distributions and a related copula model for Bayesian analysis of hypertoroidal data
This paper introduces two families of probability distributions for Bayesian analysis of hypertoroidal data. The first family consists of symmetric distributions derived from the projection of multivariate normal distributions under specific parameter constraints. This family is closed under marginalization and hence any marginal distribution belongs to a lower-dimensional case of the same family. In particular the univariate marginal of the family is the unimodal case of the projected normal d…

The fine structure of the mean magnetic field in M31
Indrajit Paul, R. Vasanth Kashyap, Tuhin Ghosh, Rainer Beck, Luke Chamandy, Srijita Sinha, Anvar Shukurov
https://arxiv.org/abs/2506.13954

The fine structure of the mean magnetic field in M31
To explore the spatial variations of the regular (mean) magnetic field of the Andromeda galaxy (M31), we use Fourier analysis in azimuthal angle along four rings in the galaxy's plane. Earlier analyses indicated that the axisymmetric magnetic field (azimuthal Fourier mode $m=0$) is sufficient to fit the observed polarization angles in a wide range of galactocentric distances. We apply a Bayesian inference approach to new, more sensitive radio continuum data at $λ\lambda3.59$, $6.18$, and $11.3…

Nonparametric Bayesian analysis for the Galton-Watson process
Massimo Cannas, Michele Guindani, Nicola Piras
https://arxiv.org/abs/2506.21304 https://

Nonparametric Bayesian analysis for the Galton-Watson process
The Galton-Watson process is a model for population growth which assumes that individuals reproduce independently according to the same offspring distribution. Inference usually focuses on the offspring average as it allows to classify the process with respect to extinction. We propose a fully non-parametric approach for Bayesian inference on the GW model using a Dirichlet Process prior. The prior naturally generalizes the Dirichlet conjugate prior distribution, and it allows learning the suppo…

Bayesian integrative factor analysis methods, with application in nutrition and genomics data
Mavis Liang, Blake Hansen, Alejandra Avalos-Pacheco, Roberta De Vito
https://arxiv.org/abs/2506.18479

Bayesian integrative factor analysis methods, with application in nutrition and genomics data
High-dimensional data are crucial in biomedical research. Integrating such data from multiple studies is a critical process that relies on the choice of advanced statistical models, enhancing statistical power, reproducibility, and scientific insight compared to analyzing each study separately. Factor analysis (FA) is a core dimensionality reduction technique that models observed data through a small set of latent factors. Bayesian extensions of FA have recently emerged as powerful tools for mu…

Statistical Machine Learning for Astronomy -- A Textbook
Yuan-Sen Ting
https://arxiv.org/abs/2506.12230 https://arxiv.org/pdf/2506.12…

Statistical Machine Learning for Astronomy -- A Textbook
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that reveals connections between modern data analysis techniques and traditional statistical methods. We show how these techniques emerge from familiar statistical foundations. The consistently Bayesian perspective prioritizes uncertainty quantification and statistical rigor essential for scientific inference in astronomy.…

Frequentist Cosmological Constraints from Full-Shape Clustering Measurements in DESI DR1
James Morawetz, Hanyu Zhang, Marco Bonici, Will Percival, Andrea Crespi, Jessica Nicole Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Francisco Javier Castander, Todd Claybaugh, Shaun Cole, Andrei Cuceu, Axel de la Macorra, Arnaud de Mattia, Biprateep Dey, Peter Doel, Simone Ferraro, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gazta\~naga, Satya Gontcho A Gontcho, Gaston Gutierrez, C…

Frequentist Cosmological Constraints from Full-Shape Clustering Measurements in DESI DR1
We perform a frequentist analysis using the standard profile likelihood method for clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI). While Bayesian inferences for Effective Field Theory models of galaxy clustering can be highly sensitive to the choice of priors for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare Bayesian and frequentist constraints for the parameter set $\{σ_8, H_0, Ω_{\rm{m}}, …

Bayesian unanchored additive models for component network meta-analysis
Augustine Wigle, Audrey B\'eliveau
https://arxiv.org/abs/2507.16047 https://

Bayesian unanchored additive models for component network meta-analysis
Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several statistical aspects of CNMA. First, by introducing a unified notation, we establish that currently available methods differ in the way they assume additivity, an important distinction that has been overlooked so far in the literature. In particular, one model uses a mo…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations
Robert M. Raddi, Tim Marshall, Vincent A. Voelz

Binary black hole population inference combining confident and marginal events from the $\tt{IAS\text{-}HM}$ search pipeline
Ajit Kumar Mehta, Digvijay Wadekar, Isha Anantpurkar, Javier Roulet, Tejaswi Venumadhav, Tousif Islam, Jonathan Mushkin, Barak Zackay, Matias Zaldarriaga
https://arxiv.org/abs/2508.15350

Binary black hole population inference combining confident and marginal events from the $\tt{IAS\text{-}HM}$ search pipeline
We present the population properties of binary black hole mergers identified by the $\tt{IAS\text{-}HM}$ pipeline (which incorporates higher-order modes in the search templates) during the third observing run (O3) of the LIGO, Virgo, and KAGRA (LVK) detectors. In our population inference analysis, instead of only using events above a sharp cut based on a particular detection threshold (e.g., false alarm rate), we use a Bayesian framework to consistently include both marginal and confident event…

Bayesian spatiotemporal modeling of passenger trip assignment in metro networks
Xiaoxu Chen, Alexandra M. Schmidt, Zhenliang Ma, Lijun Sun
https://arxiv.org/abs/2507.22403 https…

Bayesian spatiotemporal modeling of passenger trip assignment in metro networks
Assigning passenger trips to specific network paths using automatic fare collection (AFC) data is a fundamental application in urban transit analysis. The task is a difficult inverse problem: the only available information consists of each passenger's total travel time and their origin and destination, while individual passenger path choices and dynamic network costs are unobservable, and behavior varies significantly across space and time. We propose a novel Bayesian hierarchical model to reso…

Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis
Fahad Mostafa, Md Rejuan Haque, Md Mostafijur Rahman, Farzana Nasrin
https://arxiv.org/abs/2506.17852

Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis
Parameter estimation is a foundational step in statistical modeling, enabling us to extract knowledge from data and apply it effectively. Bayesian estimation of parameters incorporates prior beliefs with observed data to infer distribution parameters probabilistically and robustly. Moreover, it provides full posterior distributions, allowing uncertainty quantification and regularization, especially useful in small or truncated samples. Utilizing the left-truncated log-logistic (LTLL) distributi…

Gravitational-wave background detection using machine learning
Hugo Einsle, Marie-Anne Bizouard, Tania Regimbau, Mairi Sakellariadou
https://arxiv.org/abs/2506.14764

Gravitational-wave background detection using machine learning
Extracting the faint gravitational-wave background (GWB) signal from dominant detector noise and disentangling its %diverse astrophysical and cosmological components remain significant challenges for traditional methods like cross-correlation analysis. We propose a novel hybrid approach that combines deep learning with Bayesian inference to identify and characterize the GWB more rapidly than current techniques. Our method utilizes a custom-designed multi-scale multi-headed autoencoder (MSMHAuto…

Horseshoe Forests for High-Dimensional Causal Survival Analysis
Tijn Jacobs, Wessel N. van Wieringen, St\'ephanie L. van der Pas
https://arxiv.org/abs/2507.22004 https://

Horseshoe Forests for High-Dimensional Causal Survival Analysis
We develop a Bayesian tree ensemble model to estimate heterogeneous treatment effects in censored survival data with high-dimensional covariates. Instead of imposing sparsity through the tree structure, we place a horseshoe prior directly on the step heights to achieve adaptive global-local shrinkage. This strategy allows flexible regularisation and reduces noise. We develop a reversible jump Gibbs sampler to accommodate the non-conjugate horseshoe prior within the tree ensemble framework. We s…

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Harrison Katz, Liz Medina, Robert E. Weiss
https://arxiv.org/abs/2506.13973

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Prior choice can strongly influence Bayesian Dirichlet ARMA (B-DARMA) inference for compositional time-series. Using simulations with (i) correct lag order, (ii) overfitting, and (iii) underfitting, we assess five priors: weakly-informative, horseshoe, Laplace, mixture-of-normals, and hierarchical. With the true lag order, all priors achieve comparable RMSE, though horseshoe and hierarchical slightly reduce bias. Under overfitting, aggressive shrinkage-especially the horseshoe-suppresses noise …

Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors
Kun Fan, Srijana Subedi, Vishmi Ridmika Dissanayake Pathiranage, Cen Wu
https://arxiv.org/abs/2507.10975

Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors
Frequentist robust variable selection has been extensively investigated in high-dimensional regression. Despite success, developing the corresponding statistical inference procedures remains a challenging task. Recently, tackling this challenge from a Bayesian perspective has received much attention, as fully Bayesian analysis yields Bayesian credible intervals whose validity can be directly assessed on finite samples. In literature, the two-group spike-and-slab priors that can induce exact spa…

Extended datasamples under the lens of Brane World Theory
Kyra Jacobo, Dorian Araya
https://arxiv.org/abs/2506.15002 https://arxiv.or…

Extended datasamples under the lens of Brane World Theory
This work revises the Brane World Theory known as Randall-Sundrum with the modification of an exponential, redshift-dependent brane tension. This model is studied in a scenario assuming no dark energy, with the aim of determining whether it can reproduce the universe's acceleration on its own, without the addition of a dark energy fluid. Bayesian statistical analysis is performed in order to constrain the free parameters of each scenario using SLS, SNIa, OHD, and BAO data samples, the last two …

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
Maria Let\'icia Salvador, Gabriel Rodrigues Palma, Mariana Coelly Modesto Santos Tavares, Iran Jose Oliveira Silva, Idemauro Antonio Rodrigues de Lara
https://arxiv.org/abs/2507.17004

Modelling longitudinal polytomous animal data using Bayesian hierarchical models
The analysis of longitudinal categorical data can be complex and unfeasible due to the number of parameters involved, characterised by overparameterisation leading to model non-convergence, in addition to problems related to sample size and the presence or absence of overdispersion. In this context, we introduce Bayesian hierarchical models as an alternative methodology to classical statistical techniques for analysing nominal polytomous data in longitudinal studies. The theoretical foundation …

Convergence Rate of Efficient MCMC with Ancillarity-Sufficiency Interweaving Strategy for Panel Data Models
Makoto Nakakita, Tomoki Toyabe, Teruo Nakatsuma, Takahiro Hoshino
https://arxiv.org/abs/2507.18404

Convergence Rate of Efficient MCMC with Ancillarity-Sufficiency Interweaving Strategy for Panel Data Models
Improving Markov chain Monte Carlo algorithm efficiency is essential for enhancing computational speed and inferential accuracy in Bayesian analysis. These improvements can be effectively achieved using the ancillarity-sufficiency interweaving strategy (ASIS), an effective means of achieving such gains. Herein, we provide the first rigorous theoretical justification for applying ASIS in Bayesian hierarchical panel data models. Asymptotic analysis demonstrated that when the product of prior vari…

A Bayesian Approach to Estimating Effect Sizes in Educational Research
Yannis B\"ahni
https://arxiv.org/abs/2507.14848 https://a…

A Bayesian Approach to Estimating Effect Sizes in Educational Research
In educational research, a first step in the descriptive analysis of data from any psychometric measurement of the performance of subjects in tests at different time points and between groups is to compute relative measures of learning gains and learning achievements. For these effect sizes, there are classical approaches coming from frequentistic statistics like Student's or Welch's $t$-test with their own strengths and weaknesses. In this paper, we propose a purely Bayesian approach for analy…

Modeling Complex Life Systems: Bayesian Inference for Weibull Failure Times Using Adaptive MCMC
Tobias Oketch, Mohammad Sepehrifar
https://arxiv.org/abs/2506.11949

Modeling Complex Life Systems: Bayesian Inference for Weibull Failure Times Using Adaptive MCMC
This research develops a Bayesian framework for analyzing failure times using the Weibull distribution, addressing challenges in prior selection due to the lack of conjugate priors and multi-dimensional sufficient statistics. We propose an adaptive semi-parametric MCMC algorithm for lifetime data analysis, employing a hierarchical Bayesian model and the No-U-Turn Sampler (NUTS) in STAN. Twenty-four combinations of prior distributions are evaluated, with a noninformative LogNormal hyper-prior en…

Bayesian community detection in assortative stochastic block model with unknown number of communities
Martina Amongero, Pierpaolo De Blasi
https://arxiv.org/abs/2506.19576

Bayesian community detection in assortative stochastic block model with unknown number of communities
Structured data in the form of networks is increasingly common in a number of fields, including social sciences, biology, physics, computer science, and many others. A key task in network analysis is community detection, which typically consists of dividing the nodes into groups such that nodes within a group are strongly connected, while connections between groups are relatively scarcer. A generative model well-suited for the formation of such communities is the assortative stochastic block mo…

Bayesian Variational Inference for Mixed Data Mixture Models
Junyang Wang, James Bennett, Victor Lhoste, Sarah Filippi
https://arxiv.org/abs/2507.16545 htt…

Bayesian Variational Inference for Mixed Data Mixture Models
Heterogeneous, mixed type datasets including both continuous and categorical variables are ubiquitous, and enriches data analysis by allowing for more complex relationships and interactions to be modelled. Mixture models offer a flexible framework for capturing the underlying heterogeneity and relationships in mixed type datasets. Most current approaches for modelling mixed data either forgo uncertainty quantification and only conduct point estimation, and some use MCMC which incurs a very high…

Dynamic Latent Class Structural Equation Modeling: A Hands-On Tutorial for Modeling Intensive Longitudinal Data
Roberto Faleh, Sofia Morelli, Vivato Andriamiarana, Zachary J. Roman, Christoph Fl\"uckiger, Holger Brandt
https://arxiv.org/abs/2508.12983

Dynamic Latent Class Structural Equation Modeling: A Hands-On Tutorial for Modeling Intensive Longitudinal Data
In this tutorial, we provide a hands-on guideline on how to implement complex Dynamic Latent Class Structural Equation Models (DLCSEM) in the Bayesian software JAGS. We provide building blocks starting with simple Confirmatory Factor and Time Series analysis, and then extend these blocks to Multilevel Models and Dynamic Structural Equation Models (DSEM). Leading through the tutorial is an example from clinical psychology using data on a generalized anxiety treatment that includes scales on anxi…