Tootfinder

Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
Philipp Ertz, Petra Friederichs
https://arxiv.org/abs/2505.22182

Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
The aim of this study is to provide a probabilistic gust analysis for the region of Germany that is calibrated with station observations and with an interpolation to unobserved locations. To this end, we develop a spatial Bayesian hierarchical model (BHM) for the post-processing of surface maximum wind gusts from the COSMO-REA6 reanalysis. Our approach uses a non-stationary extreme value distribution for the gust observations, with parameters that vary according to a linear model using COSMO-RE…

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends
Wang-Ji Yan, Lin-Feng Mei, Yuan-Wei Yin, Jiang Mo, Costas Papadimitriou, Ka-Veng Yuen, Michael Beer
https://arxiv.org/abs/2505.22223

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends
Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex dynamical systems. This review meticulously traces the three-decade evolution of Bayesian learning in structural dynamics, illuminating core principles, groundbreaking methodologies, and diverse applications that have significantly influenced the field. The narrative commences by delving into the basics of …

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 …

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…

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 …

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…

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…

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…

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…

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…

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 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 …

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…

Identifying Compact Chirping SMBHBs in LSST using Bayesian Analysis
Chengcheng Xin, Maximiliano Isi, Will M. Farr, Zolt\'an Haiman
https://arxiv.org/abs/2506.10846

Identifying Compact Chirping SMBHBs in LSST using Bayesian Analysis
The Legacy Survey of Space and Time (LSST) is expected to observe up to ${\sim}100$ million quasars in the next decade. In this work, we show that it is possible to use such data to measure the characteristic frequency evolution of a "chirp" induced by gravitational waves, which can serve as robust evidence for the presence of a compact supermassive black-hole binary. Following the LSST specifications, we generate mock lightcurves consisting of (i) a post-Newtonian chirp produced by orbital mot…

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…

Efficient Bayesian multi-fidelity inverse analysis for expensive and non-differentiable physics-based simulations in high stochastic dimensions
Jonas Nitzler, Bugrahan Z. Tem\"ur, Phaedon-Stelios Koutsourelakis, Wolfgang A. Wall
https://arxiv.org/abs/2505.24708

Efficient Bayesian multi-fidelity inverse analysis for expensive and non-differentiable physics-based simulations in high stochastic dimensions
High-dimensional Bayesian inverse analysis (dim >> 100) is mostly unfeasible for computationally demanding, nonlinear physics-based high-fidelity (HF) models. Usually, the use of more efficient gradient-based inference schemes is impeded if the multi-physics models are provided by complex legacy codes. Adjoint-based derivatives are either exceedingly cumbersome to derive or non-existent for practically relevant large-scale nonlinear and coupled multi-physics problems. Similarly, holistic automa…

Reconstructing North Korea's Plutonium Production History with Bayesian Inference-Based Reprocessing Waste Analysis
Benjamin Jung, Johannes Bosse, Malte G\"ottsche
https://arxiv.org/abs/2506.03865

Reconstructing North Korea's Plutonium Production History with Bayesian Inference-Based Reprocessing Waste Analysis
Although North Korea's nuclear program has been the subject of extensive scrutiny, estimates of its fissile material stockpiles remain fraught with uncertainty. In potential future disarmament agreements, inspectors may need to use nuclear archaeology methods to verify or gain confidence in a North Korean fissile material declaration. This study explores the potential utility of a Bayesian inference-based analysis of the isotopic composition of reprocessing waste to reconstruct the operating hi…

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Gen-Liang Li, Shu-Jie Zhao, Huai-Ke Guo, Jing-Yu Su, Zhen-Heng Lin
https://arxiv.org/abs/2506.05954

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Accurate parameter estimation(PE) of gravitational waves(GW) is essential for GW data analysis. In extreme mass-ratio inspiral binary(EMRI) systems, orbital eccentricity is a critical parameter for PE. However, current software for for PE of GW often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software package for PE of GW with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge (AK) waveform as a computat…

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 …

Observational Insights on DBI K-essence Models Using Machine Learning and Bayesian Analysis
Samit Ganguly, Arijit Panda, Eduardo Guendelman, Debashis Gangopadhyay, Abhijit Bhattacharyya, Goutam Manna
https://arxiv.org/abs/2506.05674

Observational Insights on DBI K-essence Models Using Machine Learning and Bayesian Analysis
We present a comparative observational data analysis of two DBI-type k-essence scalar field models for late-time cosmic acceleration: one treats dark energy with standard matter, while the other unifies dark energy and dark matter within a single scalar field, each governed by distinct non-canonical Lagrangians. The background dynamics are formulated via modified Friedmann and scalar field equations, constrained by Pantheon+ SNe Ia, Hubble, and BAO data, using Bayesian inference (NUTS in NumPyr…

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…

This https://arxiv.org/abs/2407.18293 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

On the relation between magnetic field strength and gas density in the interstellar medium: A multiscale analysis
The magnetic field strength to gas density relation in the interstellar medium is of fundamental importance. We present and compare Bayesian analyses of the B-n relation for two comprehensive observational data sets: a Zeeman data set and 700 observations using the Davis-Chandrasekhar-Fermi (DCF) method. Using a hierarchical Bayesian analysis we present a general, multi-scale broken power-law relation, $B=B_0(n/n_0)^α$ , with $α=α_1$ for $nn_0$, and with $B_0$ the fie…

Analysis of in-vivo skin anisotropy using elastic wave measurements and Bayesian modelling
Matt Nagle, Susan Price, Antonia Trotta, Michel Destrade, Michael Fop, Aisling N\'i Annaidh
https://arxiv.org/abs/2506.02248

Analysis of in-vivo skin anisotropy using elastic wave measurements and Bayesian modelling
In vivo skin exhibits viscoelastic, hyper-elastic and non-linear characteristics. It is under a constant non-equibiaxial tension in its natural configuration and is reinforced with oriented collagen fibers, giving rise to anisotropic behaviour. Understanding the complex mechanical behaviour of skin has relevance across many sectors including pharmaceuticals, cosmetics and surgery. However, there is a dearth of quality data characterizing human skin anisotropy in vivo. The available data is usua…

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.…

Large Bayesian VARs for Binary and Censored Variables
Joshua C. C. Chan, Michael Pfarrhofer
https://arxiv.org/abs/2506.01422 https://…

Large Bayesian VARs for Binary and Censored Variables
We extend the standard VAR to jointly model the dynamics of binary, censored and continuous variables, and develop an efficient estimation approach that scales well to high-dimensional settings. In an out-of-sample forecasting exercise, we show that the proposed VARs forecast recessions and short-term interest rates well. We demonstrate the utility of the proposed framework using a wide rage of empirical applications, including conditional forecasting and a structural analysis that examines the…

Multi-wavelength analysis of FSRQ B2 1348 30B: Constraints on the jet power
Sajad Ahanger, Sunder Sahayanathan, Sitha K. Jagan, Shah Zahir, Naseer Iqbal
https://arxiv.org/abs/2506.02821

Multi-wavelength analysis of FSRQ B2 1348+30B: Constraints on the jet power
We present 14.5-year multi-wavelength analysis of flat-spectrum radio quasar B2 1348+30B using Swift-UVOT, Swift-XRT, and Fermi-LAT observations. In the gamma-ray band, the 3 day bin lightcurve reveals two major flaring events on 2010-09-19 (55458 MJD) and 2022-05-26 (59725 MJD) detected at flux levels $(2.5\pm 0.5) \times 10^{-7}\,\rm{ph\,cm^{-2}\,s^{-1}}$ and $(5.2\pm 0.6) \times 10^{-7}\,\rm{ph\,cm^{-2}\,s^{-1}}$. The Bayesian block analysis of the flares suggested the variability timescale …

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…

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…

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…

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…

Constraining Dark Matter Parameters in the Lambda-CDM Framework: A Bayesian Comparison of Planck and DES Constraints
Manjeet Kunwar, Nabin Bhusal, Manil Khatiwada, Niraj Dhital
https://arxiv.org/abs/2506.07133

Constraining Dark Matter Parameters in the Lambda-CDM Framework: A Bayesian Comparison of Planck and DES Constraints
We present a Bayesian analysis of cosmological parameter constraints from early- and late-universe observations, focusing on the matter density parameter ($Ω_m$) and the amplitude of matter fluctuations ($σ_8$) within the $Λ$CDM framework. Using data from the Planck 2018 satellite mission and the Dark Energy Survey (DES) Year 3, we compute theoretical predictions for angular and matter power spectra via Boltzmann solvers and perform Markov Chain Monte Carlo (MCMC) sampling using the \texttt{…

Limiting the Shrinkage for the Exceptional by Objective Robust Bayesian Analysis: the `Clemente Problem'
Luis R. Pericchi, Maria-Eglee Perez
https://arxiv.org/abs/2506.10114

Limiting the Shrinkage for the Exceptional by Objective Robust Bayesian Analysis: the `Clemente Problem'
In `borrowing strength' an important problem of Statistics is to treat exceptional cases in a fundamentally different. This is what has been coined as `the Clemente problem' in honor of R. Clemente (Efron 2010). In this article, we propose to use robust penalties, in the form of losses that penalize more severely huge errors, or (equivalently) priors of heavy tails which make more probable the exceptional. Using heavy tailed priors, we can reproduce in a Bayesian way Efron and Morris `limited t…

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Gen-Liang Li, Shu-Jie Zhao, Huai-Ke Guo, Jing-Yu Su, Zhen-Heng Lin
https://arxiv.org/abs/2506.05954

MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Accurate parameter estimation(PE) of gravitational waves(GW) is essential for GW data analysis. In extreme mass-ratio inspiral binary(EMRI) systems, orbital eccentricity is a critical parameter for PE. However, current software for for PE of GW often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software package for PE of GW with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge (AK) waveform as a computat…

Symbolic Higher-Order Analysis of Multivariate Time Series
Andrea Civilini, Fabrizio de Vico Fallani, Vito Latora
https://arxiv.org/abs/2506.00508 https://…

Symbolic Higher-Order Analysis of Multivariate Time Series
Identifying patterns of relations among the units of a complex system from measurements of their activities in time is a fundamental problem with many practical applications. Here, we introduce a method that detects dependencies of any order in multivariate time series data. The method first transforms a multivariate time series into a symbolic sequence, and then extract statistically significant strings of symbols through a Bayesian approach. Such motifs are finally modelled as the hyperedges …

A Bayesian analysis of home advantage in professional squash
Philip Greengard, Samer Takriti
https://arxiv.org/abs/2506.09287 https://

A Bayesian analysis of home advantage in professional squash
We estimate the effect of playing in one's home country in professional squash using a Bayesian hierarchical model applied to men's and women's Professional Squash Association matches from 2018-2024. The model incorporates players' world rankings and whether they are competing in their home country. Using margin of victory in games as our outcome, we estimate that home advantage adds 0.4 games for men and 0.3 games for women to the expected margin, with standard errors of 0.1. For evenly matche…

This https://arxiv.org/abs/2505.22223 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends
Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex dynamical systems. This review meticulously traces the three-decade evolution of Bayesian learning in structural dynamics, illuminating core principles, groundbreaking methodologies, and diverse applications that have significantly influenced the field. The narrative commences by delving into the basics of …

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 …

This https://arxiv.org/abs/1910.14012 has been replaced.
link: https://scholar.google.com/scholar?q=a

$\texttt{HEPfit}$: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models
$\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to $\textit{i)}$ fit the model parameters to a given set of experimental observables; $\textit{ii)}$ obtain predictions for observables. $\texttt{HEPfit}$ can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowin…

Inferring the pair-instability mass gap from gravitational wave data using flexible models
Fabio Antonini, Thomas Callister, Fani Dosopoulou, Isobel Romero-Shaw, Debatri Chattopadhyay
https://arxiv.org/abs/2506.09154

Inferring the pair-instability mass gap from gravitational wave data using flexible models
We use hierarchical Bayesian inference with non-parametric Gaussian process models to investigate the effective inspiral spin parameter, $χ_{\rm eff}$, as a function of primary black hole mass in the third gravitational-wave transient catalog (GWTC-3). Our analysis reveals a transition in the population at a primary mass of $46^{+7}_{-5}\,M_\odot$. Beyond this mass, the $χ_{\rm eff}$ distribution broadens, becomes consistent with being symmetric around zero, and has a median of $-0.03^{+0.36}…

A Bayesian PINN Framework for Barrow-Tsallis Holographic Dark Energy with Neutrinos: Toward a Resolution of the Hubble Tension
Muhammad Yarahmadi, Amin Salehi
https://arxiv.org/abs/2506.02235

A Bayesian PINN Framework for Barrow-Tsallis Holographic Dark Energy with Neutrinos: Toward a Resolution of the Hubble Tension
We investigate the Barrow-Tsallis Holographic Dark Energy (BTHDE) model using both traditional Markov Chain Monte Carlo (MCMC) methods and a Bayesian Physics-Informed Neural Network (PINN) framework, employing a range of cosmological observations. Our analysis incorporates data from Cosmic Microwave Background (CMB), Baryon Acoustic Oscillations (BAO), CMB lensing, Cosmic Chronometers (CC), and the Pantheon+ Type Ia supernova compilation. We focus on constraining the Hubble constant $ H_0 $, th…

Efficient Gibbs Sampling in Cox Regression Models Using Composite Partial Likelihood and P\'olya-Gamma Augmentation
Shu Tamano, Yui Tomo
https://arxiv.org/abs/2506.04675

Efficient Gibbs Sampling in Cox Regression Models Using Composite Partial Likelihood and Pólya-Gamma Augmentation
The Cox regression model and its Bayesian extensions are widely used in survival analysis. However, standard Bayesian approaches require modeling of the baseline hazard, and their full conditional distributions lack closed-form expressions. Therefore, the Metropolis-Hastings sampling algorithm is typically employed, whose efficiency is highly sensitive to the choice of proposal distribution. To address these issues, we propose the GS4Cox, an efficient Gibbs sampling algorithm for the Cox regres…

This https://arxiv.org/abs/2503.03076 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Non-Affine Extensions of the Raychaudhuri Equation in the K-essence Framework
We present a new avenue of the Raychaudhuri Equation (RE) by introducing a non-affine parametrization within the k-essence framework. This modification accounts for non-geodesic flow curves, leading to emergent repulsive effects in cosmic evolution. Using a DBI-type k-essence Lagrangian, we derive a modified RE and demonstrate its ability to address the Hubble tension while predicting a natural emergence of a dynamical dark energy equation of state. Our Bayesian analysis, constrained by cosmolo…

This https://arxiv.org/abs/2411.16505 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Pre-Big-Bang Cosmology Cannot Explain NANOGrav 15-year Signal
We investigate whether the Pre-Big Bang (PBB) scenario from string cosmology can explain the stochastic gravitational wave background signal reported in the NANOGrav 15-year dataset. Using Bayesian analysis techniques, we constrain the key parameters of the PBB model by comparing its theoretical predictions with the observed data. Our analysis yields $β= -0.12^{+0.06}_{-0.21}$ ($90\%$ credible interval) for the dilaton-dynamics parameter, which lies outside the theoretically allowed range $0 \…

Variational inference for steady-state BVARs
Oskar Gustafsson, Mattias Villani
https://arxiv.org/abs/2506.09271 https://arxiv.org/pdf…

Variational inference for steady-state BVARs
The steady-state Bayesian vector autoregression (BVAR) makes it possible to incorporate prior information about the long-run mean of the process. This has been shown in many studies to substantially improve forecasting performance, and the model is routinely used for forecasting and macroeconomic policy analysis at central banks and other financial institutions. Steady-steady BVARs are estimated using Gibbs sampling, which is time-consuming for the increasingly popular large-scale BVAR models w…

Cosmological Dynamics of Accelerating Model in $f(T)$ Gravity with Special Form of Deceleration Parameter
Shivank Pal, Romanshu Garg, Gyan Prakash Singh, Gauree Shanker
https://arxiv.org/abs/2506.03756

Cosmological Dynamics of Accelerating Model in $f(T)$ Gravity with Special Form of Deceleration Parameter
In this paper, the dynamical behavior of the accelerated expansion of the universe is discussed within the framework of $f(T)$ gravity for a particular form of $ f(T)=α(-T)^{n}$. Considering a redshift-dependent parameterization of the deceleration parameter as $q(z)=q_{0}+ q_{1}\left(\frac{ \ln(N+z)}{z+1}-\ln N \right)$. We have derived the Hubble parameter in terms of redshift and discussed its effect on other cosmological parameters. Using Bayesian statistical analysis and $χ^2$-minimizati…

Differentiable Fuzzy Cosmic-Web for Field Level Inference
P. Rossell\'o, F. -S. Kitaura, D. Forero-S\'anchez, F. Sinigaglia, G. Favole
https://arxiv.org/abs/2506.03969

Differentiable Fuzzy Cosmic-Web for Field Level Inference
A comprehensive analysis of the cosmological large-scale structure derived from galaxy surveys involves field-level inference, which requires a forward modelling framework that simultaneously accounts for structure formation and tracer bias. While structure formation models are well-understood, the development of an effective field-level bias model remains imprecise, particularly in the context of tracer perturbation theory within Bayesian reconstruction methods, which we address in this work. …

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Constraints on AvERA Cosmologies from Cosmic Chronometers and Type Ia Supernovae
We constrain AvERA cosmologies in comparison with the flat $Λ$CDM model using cosmic chronometer (CC) data and the Pantheon+ sample of type Ia supernovae (SNe Ia). The analysis includes fits to both CC and SN datasets using the \texttt{dynesty} dynamic nested sampling algorithm. For model comparison, we use the Bayesian model evidences and Anderson-Darling tests applied to the normalized residuals to assess consistency with a standard normal distribution. Best-fit parameters are derived within…