Tootfinder

Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis
Anna Elisabeth Riha, Nikolas Siccha, Antti Oulasvirta, Aki Vehtari
https://arxiv.org/abs/2404.01688

Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis
When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate models. In particular, checks and evaluations throughout the modelling process can motivate changes to an existing model or the consideration of alternative models to ultimately obtain models of sufficient quality for the problem at hand. Additionally, failing to consider alternative models can lead to overconfidence i…

Generation and analysis of synthetic data via Bayesian networks: a robust approach for uncertainty quantification via Bayesian paradigm
Larissa N. A. Martins, Fl\'avio B. Gon\c{c}alves, Thais P. Galletti
https://arxiv.org/abs/2402.17915

Generation and analysis of synthetic data via Bayesian networks: a robust approach for uncertainty quantification via Bayesian paradigm
Safe and reliable disclosure of information from confidential data is a challenging statistical problem. A common approach considers the generation of synthetic data, to be disclosed instead of the original data. Efficient approaches ought to deal with the trade-off between reliability and confidentiality of the released data. Ultimately, the aim is to be able to reproduce as accurately as possible statistical analysis of the original data using the synthetic one. Bayesian networks is a model-b…

This https://arxiv.org/abs/2402.11101 has been replaced.
initial toot: https://mastoxiv.page/@a…

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experi…

Induced Gravitational Wave interpretation of PTA data: a complete study for general equation of state
Guillem Dom\`enech, Shi Pi, Ao Wang, Jianing Wang
https://arxiv.org/abs/2402.18965

Induced Gravitational Wave interpretation of PTA data: a complete study for general equation of state
We thoroughly study the induced gravitational wave interpretation of the possible gravitational wave background reported by PTA collaborations, considering the unknown equation of state $w$ of the early universe. We perform a Bayesian analysis of the NANOGrav data using the publicly available \textsc{PTArcade} code together with \textsc{SIGWfast} for the numerical integration of the induced gravitational wave spectrum. We focus on two cases: a monochromatic and a log-normal primordial spectrum …

Constrained hidden Markov models reveal further Hsp90 protein states
Riccardo Tancredi, Antonio Feltrin, Giosu\`e Sardo Infirri, Simone Toso, Leonie Vollmar, Thorsten Hugel, Marco Baiesi
https://arxiv.org/abs/2402.19207

Constrained hidden Markov models reveal further Hsp90 protein states
Time series of conformational dynamics in proteins are usually evaluated with hidden Markov models (HMMs). This approach works well if the number of states and their connectivity is known. But for the multi-domain protein Hsp90, a standard HMM analysis with optimization of the BIC (Bayesian information criterion) cannot explain long-lived states well. Therefore, here we employ constrained hidden Markov models, which neglect transitions between states by including assumptions. Gradually tuning a…

Supervised Bayesian joint graphical model for simultaneous network estimation and subgroup identification
Xing Qin, Xu Liu, Shuangge Ma, Mengyun Wu
https://arxiv.org/abs/2403.19994

Supervised Bayesian joint graphical model for simultaneous network estimation and subgroup identification
Heterogeneity is a fundamental characteristic of cancer. To accommodate heterogeneity, subgroup identification has been extensively studied and broadly categorized into unsupervised and supervised analysis. Compared to unsupervised analysis, supervised approaches potentially hold greater clinical implications. Under the unsupervised analysis framework, several methods focusing on network-based subgroup identification have been developed, offering more comprehensive insights than those restricte…

Bayesian Survival Analysis by Approximate Inference of Neural Networks
Christian Marius Lillelund, Martin Magris, Christian Fischer Pedersen
https://arxiv.org/abs/2404.06421

Bayesian Survival Analysis by Approximate Inference of Neural Networks
Predicting future events always comes with uncertainty, but traditional non-Bayesian methods cannot distinguish certain from uncertain predictions or explain the confidence in their predictions. In survival analysis, Bayesian methods applied to state-of-the-art solutions in the healthcare and biomedical field are still novel, and their implications have not been fully evaluated. In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a foc…

Notes on the Practical Application of Nested Sampling: MultiNest, (Non)convergence, and Rectification
Alexander J. Dittmann
https://arxiv.org/abs/2404.16928

Notes on the Practical Application of Nested Sampling: MultiNest, (Non)convergence, and Rectification
Nested sampling is a promising tool for Bayesian statistical analysis because it simultaneously performs parameter estimation and facilitates model comparison. MultiNest is one of the most popular nested sampling implementations, and has been applied to a wide variety of problems in the physical sciences. However, MultiNest results are frequently unreliable, and accompanying convergence tests are a necessary component of any analysis. Using simple, analytically tractable test problems, I illust…

Bayesian Analysis of Conventional and Ultrafast Spectroscopy Data for Investigating Detachment in the MAST-Upgrade Super-X
Xander Pope, Kevin Verhaegh, Chris Bowman, Bruce Lipschultz
https://arxiv.org/abs/2404.15072

Bayesian Analysis of Conventional and Ultrafast Spectroscopy Data for Investigating Detachment in the MAST-Upgrade Super-X
This paper presents the application, testing and first results of a new adaptive Bayesian inference analysis which utilises conventional and ultrafast spectroscopic measurements made in the divertor chamber to investigate the divertor physics during detachment. Validation of this software is performed prior and during analyses of results, demonstrated by compelling reproductions of ideal test cases and synthetic spectroscopic measurements. Application on real diagnostic data shows strong agreem…

This https://arxiv.org/abs/2308.03456 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Bayesian inference from gravitational waves in fast-rotating, core-collapse supernovae
Core-collapse supernovae (CCSNe) are prime candidates for gravitational-wave detectors. The analysis of their complex waveforms can potentially provide information on the physical processes operating during the collapse of the iron cores of massive stars. In this work we analyze the early-bounce rapidly rotating CCSN signals reported in the waveform catalog of Richers et al 2017, which comprises over 1800 axisymmetric simulations extending up to about 10~ms of post-bounce evolution. It was prev…

Quasi-likelihood analysis for adaptive estimation of a degenerate diffusion process
Arnaud Gloter, Nakahiro Yoshida
https://arxiv.org/abs/2402.15256 https:…

Quasi-likelihood analysis for adaptive estimation of a degenerate diffusion process
The adaptive quasi-likelihood analysis is developed for a degenerate diffusion process. Asymptotic normality and moment convergence are proved for the quasi-maximum likelihood estimators and quasi-Bayesian estimators, in the adaptive scheme.

This https://arxiv.org/abs/2402.11101 has been replaced.
initial toot: https://mastoxiv.page/@a…

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experi…

Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Hao Hao, Xiaoqun Zhang, Aimin Zhou
https://arxiv.org/abs/2403.14413

Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for simulations. Bayesian Optimization (BO) and Surrogate-Assisted Evolutionary Algorithm (SAEA) are two widely used gradient-free optimization techniques employed to address such challenges. Both approaches follow a similar iterative procedure that rel…

On the Improvement of Predictive Modeling Using Bayesian Stacking and Posterior Predictive Checking
Mariana Nold, Florian Meinfelder, David Kaplan
https://arxiv.org/abs/2402.19046

On the Improvement of Predictive Modeling Using Bayesian Stacking and Posterior Predictive Checking
Model uncertainty is pervasive in real world analysis situations and is an often-neglected issue in applied statistics. However, standard approaches to the research process do not address the inherent uncertainty in model building and, thus, can lead to overconfident and misleading analysis interpretations. One strategy to incorporate more flexible models is to base inferences on predictive modeling. This approach provides an alternative to existing explanatory models, as inference is focused o…

Measuring Solar Neutrinos in the SNO Detector
Daniel Cookman
https://arxiv.org/abs/2403.19532 https://arxiv.org/pdf/2403.19532

Measuring Solar Neutrinos in the SNO+ Detector
The SNO+ experiment is a large multi-purpose neutrino detector, currently filled with liquid scintillator. For the first time in a single experiment, SNO+ is able to measure the neutrino oscillation parameters $θ_{12}$ and $Δm^{2}_{21}$ simultaneously through both reactor anti-neutrinos and $^{8}B$ solar neutrinos. The latter approach is demonstrated here, with an analysis of an initial 80 days of scintillator phase data. A Bayesian statistical approach via Markov Chain Monte Carlo is used, a…

On the Pettis Integral Approach to Large Population Games
Masaki Miyashita, Takashi Ui
https://arxiv.org/abs/2403.17605 https://arxiv…

On the Pettis Integral Approach to Large Population Games
The analysis of large population economies with incomplete information often entails the integration of a continuum of random variables. We showcase the usefulness of the integral notion à la Pettis (1938) to study such models. We present several results on Pettis integrals, including convenient sufficient conditions for Pettis integrability and Fubini-like exchangeability formulae, illustrated through a running example. Building on these foundations, we conduct a unified analysis of Bayesian …

Stochastic Volatility in Mean: Efficient Analysis by a Generalized Mixture Sampler
Daichi Hiraki, Siddhartha Chib, Yasuhiro Omori
https://arxiv.org/abs/2404.13986

Stochastic Volatility in Mean: Efficient Analysis by a Generalized Mixture Sampler
In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in Kim et al. (1998) and Omori et al. (2007), we develop an accurate approximation of the non-central chi-squared distribution as a mixture of thirty normal distributions. Under this mixture representation, we sample the parameters and latent volatilities in one block. We also detail a co…

Probing the shape of the quark-gluon plasma droplet via event-by-event QGP tomography
Bithika Karmakar, Dusan Zigic, Pasi Huovinen, Marko Djordjevic, Magdalena Djordjevic, Jussi Auvinen
https://arxiv.org/abs/2403.17817

Probing the shape of the quark-gluon plasma droplet via event-by-event QGP tomography
This study investigates Quark-Gluon Plasma (QGP) in heavy-ion collisions through two avenues: high-$p_{\perp}$ frameworks and hydrodynamic modeling. Using the T$_{\text{R}}$ENTo model, we find that IP-Glasma mimicking $p=0$ value aligns well with high-$p_{\perp}$ data, in agreement with Bayesian analysis of the low-$p_{\perp}$ regime. While adjusting $p$ values may improve a fit to a particular high-$p_{\perp}$ observable, it does not permit an earlier onset of transverse expansion.

A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier-Stokes Equations
Animikh Biswas, Michal Branicki
https://arxiv.org/abs/2402.14078

A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier-Stokes Equations
Bayesian state estimation of a dynamical system utilising a stream of noisy measurements is important in many geophysical and engineering applications. We establish rigorous results on (time-asymptotic) accuracy and stability of these algorithms with general covariance and observation operators. The accuracy and stability results for EnKF and EnSRKF for dissipative PDEs are, to the best of our knowledge, completely new in this general setting. It turns out that a hitherto unexploited cancellati…

Bayesian Geographically Weighted Regression using Fused Lasso Prior
Toshiki Sakai, Jun Tsuchida, Hiroshi Yadohisa
https://arxiv.org/abs/2402.18186 https://…

Bayesian Geographically Weighted Regression using Fused Lasso Prior
A main purpose of spatial data analysis is to predict the objective variable for the unobserved locations. Although Geographically Weighted Regression (GWR) is often used for this purpose, estimation instability proves to be an issue. To address this issue, Bayesian Geographically Weighted Regression (BGWR) has been proposed. In BGWR, by setting the same prior distribution for all locations, the coefficients' estimation stability is improved. However, when observation locations' density is spat…

This https://arxiv.org/abs/2404.10427 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Effect of Systematic Uncertainties on Density and Temperature Estimates in Coronae of Capella
We estimate the coronal density of Capella using the O VII and Fe XVII line systems in the soft X-ray regime that have been observed over the course of the Chandra mission. Our analysis combines measures of error due to uncertainty in the underlying atomic data with statistical errors in the Chandra data to derive meaningful overall uncertainties on the plasma density of the coronae of Capella. We consider two Bayesian frameworks. First, the so-called pragmatic-Bayesian approach considers the a…

This https://arxiv.org/abs/2304.09933 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement
This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional weighted inner product space. We demonstrate the benefit of working on a weighted space by establishing operator-norm bounds for finite element and graph-based discretizations of Matérn-type priors and deconvolution forward models. For linear-Gaussian inverse problems, we develop a general theory to characterize the …

Bayesian electron density determination from sparse and noisy single-molecule X-ray scattering images
Steffen Schultze, Helmut Grubm\"uller
https://arxiv.org/abs/2403.18391 <…

Bayesian electron density determination from sparse and noisy single-molecule X-ray scattering images
Single molecule X-ray scattering experiments using free electron lasers hold the potential to resolve both single structures and structural ensembles of biomolecules. However, molecular electron density determination has so far not been achieved due to low photon counts, high noise levels and low hit rates. Most analysis approaches therefore focus on large specimen like entire viruses, which scatter substantially more photons per image, such that it becomes possible to determine the molecular o…

This https://arxiv.org/abs/2402.01940 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Study of the Generalized Naming Game and Bayesian Naming Game as Dynamical Systems
We study the $β$-model ($β$-NG) and the Bayesian Naming Game (BNG) as dynamical systems. By applying linear stability analysis to the dynamical system associated with the $β$-model, we demonstrate the existence of a non-generic bifurcation with a bifurcation point $β_c = 1/3$. As $β$ passes through $β_c$, the stability of isolated fixed points changes, giving rise to a one-dimensional manifold of fixed points. Notably, this attracting invariant manifold forms an arc of an ellipse. In the …

Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics
Laura J. Johnston, Takoua Jendoubi
https://arxiv.org/abs/2403.14686

Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics
In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on increasing student engagement. A longitudinal overview of resource interactions is mapped through Bayesian network analysis of Moodle ac…

Two-dimensional inference of divertor plasma characteristics: advancements to a multi-instrument Bayesian analysis system
Daniel Greenhouse, Chris Bowman, Bruce Lipschultz, Kevin Verhaegh, James Harrison, Alexandre Fil
https://arxiv.org/abs/2403.12819

Two-dimensional inference of divertor plasma characteristics: advancements to a multi-instrument Bayesian analysis system
An integrated data analysis system based on Bayesian inference has been developed for application to data from multiple diagnostics over the two-dimensional cross-section of tokamak divertors. Tests of the divertor multi-instrument Bayesian analysis system (D-MIBAS) on a synthetic data set (including realistic experimental uncertainties) generated from SOLPS-ITER predictions of the MAST-U divertor have been performed. The resulting inference was within 6\%, 5\% and 30\% median absolute percenta…

Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints
Abdelmajid Ben Yahya, Santiago Ramos Garces, Nick Van Oosterwyck, Annie Cuyt, Stijn Derammelaere
https://arxiv.org/abs/2403.08473

Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints
This research delves into optimizing mechanism design, with an emphasis on the energy efficiency and the expansive design possibilities of reciprocating mechanisms. It investigates how to efficiently integrate Computer-Aided Design (CAD) simulations with Bayesian Optimization (BO) and a constrained design space, aiming to enhance the design optimization process beyond the confines of traditional kinematic and dynamic analysis. The study sets out to create a novel optimization framework that mer…

Investigating the Hard State of MAXI J1820 070: A Comprehensive Bayesian Approach to Black Hole Spin and Accretion Properties
Sachin D. Dias, Simon Vaughan, Mehdy Lefkir, Graham Wynn
https://arxiv.org/abs/2402.11380

Investigating the Hard State of MAXI J1820+070: A Comprehensive Bayesian Approach to Black Hole Spin and Accretion Properties
We analyse the X-ray spectrum of the black hole X-ray binary MAXI J1820+070 using observations from XMM-Newton and NuSTAR during 'hard' states of its 2018-2019 outburst. We take a fully Bayesian approach, and this is one of the first papers to present a fully Bayesian workflow for the analysis of an X-ray binary X-ray spectrum. This allows us to leverage the relatively well-understood distance and binary system properties (like inclination and black hole mass), as well as information from the X…

This https://arxiv.org/abs/2403.14413 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for simulations. Bayesian Optimization (BO) and Surrogate-Assisted Evolutionary Algorithm (SAEA) are two widely used gradient-free optimization techniques employed to address such challenges. Both approaches follow a similar iterative procedure that rel…

Does spacetime have memories? Searching for gravitational-wave memory in the third LIGO-Virgo-KAGRA gravitational-wave transient catalogue
Shun Yin Cheung, Paul D. Lasky, Eric Thrane
https://arxiv.org/abs/2404.11919

Does spacetime have memories? Searching for gravitational-wave memory in the third LIGO-Virgo-KAGRA gravitational-wave transient catalogue
Gravitational-wave memory is a non-linear effect predicted by general relativity that remains undetected. We apply a Bayesian analysis framework to search for gravitational-wave memory using binary black hole mergers in LIGO-Virgo-KAGRA's third gravitational-wave transient catalogue. We obtain a Bayes factor of $\ln \text{BF}=0.01$, in favour of the no-memory hypothesis, which implies that we are unable to measure memory with currently available data. This is consistent with previous work, sugg…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
Maxime HerbrichIPHC, Eythan CousinIPHC, Ivan Puga-GonzalezIPHC, Barbara TiddiIPHC, Claudia FichtelIPHC, Meg CrofootIPHC, Andrew Jj MacintoshIPHC, Erica van de WaalIPHC, C\'edric SueurIPHC
https://arxiv.org/abs/2402.13658…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
This study investigates the prevalence and implications of nestedness within primate social networks, examining its relationship with cognitive and structural factors. We analysed data from 51 primate groups across 21 species, employing network analysis to evaluate nestedness and its correlation with modularity, neocortex ratio, and group size. We used Bayesian mixed effects modelling to investigate nestedness in primate social networks, controlling for phylogenetic dependencies and exploring v…

This https://arxiv.org/abs/2205.08010 has been replaced.
link: https://scholar.google.com/scholar?q=a

The e-value and the Full Bayesian Significance Test: Logical Properties and Philosophical Consequences
This article gives a conceptual review of the e-value, ev(H|X) -- the epistemic value of hypothesis H given observations X. This statistical significance measure was developed in order to allow logically coherent and consistent tests of hypotheses, including sharp or precise hypotheses, via the Full Bayesian Significance Test (FBST). Arguments of analysis allow a full characterization of this statistical test by its logical or compositional properties, showing a mutual complementarity betw…

Two-dimensional inference of divertor plasma characteristics: advancements to a multi-instrument Bayesian analysis system
Daniel Greenhouse, Chris Bowman, Bruce Lipschultz, Kevin Verhaegh, James Harrison, Alexandre Fil
https://arxiv.org/abs/2403.12819

Two-dimensional inference of divertor plasma characteristics: advancements to a multi-instrument Bayesian analysis system
An integrated data analysis system based on Bayesian inference has been developed for application to data from multiple diagnostics over the two-dimensional cross-section of tokamak divertors. Tests of the divertor multi-instrument Bayesian analysis system (D-MIBAS) on a synthetic data set (including realistic experimental uncertainties) generated from SOLPS-ITER predictions of the MAST-U divertor have been performed. The resulting inference was within 6\%, 5\% and 30\% median absolute percenta…

Sequential design for surrogate modeling in Bayesian inverse problems
Paul Lartaud, Philippe Humbert, Josselin Garnier
https://arxiv.org/abs/2402.16520 htt…

Sequential design for surrogate modeling in Bayesian inverse problems
Sequential design is a highly active field of research in active learning which provides a general framework for the design of computer experiments to make the most of a low computational budget. It has been widely used to generate efficient surrogate models able to replace complex computer codes, most notably for uncertainty quantification, Bayesian optimization, reliability analysis or model calibration tasks. In this work, a sequential design strategy is developed for Bayesian inverse proble…

Probing the shape of the quark-gluon plasma droplet via event-by-event QGP tomography
Bithika Karmakar, Dusan Zigic, Pasi Huovinen, Marko Djordjevic, Magdalena Djordjevic, Jussi Auvinen
https://arxiv.org/abs/2403.17817

Probing the shape of the quark-gluon plasma droplet via event-by-event QGP tomography
This study investigates Quark-Gluon Plasma (QGP) in heavy-ion collisions through two avenues: high-$p_{\perp}$ frameworks and hydrodynamic modeling. Using the T$_{\text{R}}$ENTo model, we find that IP-Glasma mimicking $p=0$ value aligns well with high-$p_{\perp}$ data, in agreement with Bayesian analysis of the low-$p_{\perp}$ regime. While adjusting $p$ values may improve a fit to a particular high-$p_{\perp}$ observable, it does not permit an earlier onset of transverse expansion.

Investigating the Hard State of MAXI J1820 070: A Comprehensive Bayesian Approach to Black Hole Spin and Accretion Properties
Sachin D. Dias, Simon Vaughan, Mehdy Lefkir, Graham Wynn
https://arxiv.org/abs/2402.11380

Investigating the Hard State of MAXI J1820+070: A Comprehensive Bayesian Approach to Black Hole Spin and Accretion Properties
We analyse the X-ray spectrum of the black hole X-ray binary MAXI J1820+070 using observations from XMM-Newton and NuSTAR during 'hard' states of its 2018-2019 outburst. We take a fully Bayesian approach, and this is one of the first papers to present a fully Bayesian workflow for the analysis of an X-ray binary X-ray spectrum. This allows us to leverage the relatively well-understood distance and binary system properties (like inclination and black hole mass), as well as information from the X…

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
Hualin Zhan, Viqar Ahmad, Azul Mayon, Grace Tabi, Anh Dinh Bui, Zhuofeng Li, Daniel Walters, Hieu Nguyen, Klaus Weber, Thomas White, Kylie Catchpole
https://arxiv.org/abs/2402.11101

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experi…

This https://arxiv.org/abs/2303.03521 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Bayesian Variable Selection for Function-on-Scalar Regression Models: a comparative analysis
In this work, we developed a new Bayesian method for variable selection in function-on-scalar regression (FOSR). Our method uses a hierarchical Bayesian structure and latent variables to enable an adaptive covariate selection process for FOSR. Extensive simulation studies show the proposed method's main properties, such as its accuracy in estimating the coefficients and high capacity to select variables correctly. Furthermore, we conducted a substantial comparative analysis with the main compet…

This https://arxiv.org/abs/2305.10367 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Truncated Partial-Wave Analysis for $η$-photoproduction observables via Bayesian Statistics
A truncated partial-wave analysis is performed for $η$-photoproduction using the polarization observables $σ_0, Σ, T, E, F$ and $G$. Different truncation orders are analyzed for six energy bins within the range of $E^{lab}_γ \in [750, 1250]$ MeV. Bayesian statistics is combined with truncated partial-wave analysis for the first time to investigate the structure of emerging ambiguities and their relevance in comparison to each other. Marginal distributions for the electromagnetic multipole p…

Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints
Abdelmajid Ben Yahya, Santiago Ramos Garces, Nick Van Oosterwyck, Annie Cuyt, Stijn Derammelaere
https://arxiv.org/abs/2403.08473

Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints
This research delves into optimizing mechanism design, with an emphasis on the energy efficiency and the expansive design possibilities of reciprocating mechanisms. It investigates how to efficiently integrate Computer-Aided Design (CAD) simulations with Bayesian Optimization (BO) and a constrained design space, aiming to enhance the design optimization process beyond the confines of traditional kinematic and dynamic analysis. The study sets out to create a novel optimization framework that mer…

Effect of Systematic Uncertainties on Density and Temperature Estimates in Coronae of Capella
Xixi Yu, Vinay L. Kashyap, Giulio Del Zanna, David A. van Dyk, David C. Stenning, Connor P. Ballance, Harry P. Warren
https://arxiv.org/abs/2404.10427

Effect of Systematic Uncertainties on Density and Temperature Estimates in Coronae of Capella
We estimate the coronal density of Capella using the O VII and Fe XVII line systems in the soft X-ray regime that have been observed over the course of the Chandra mission. Our analysis combines measures of error due to uncertainty in the underlying atomic data with statistical errors in the Chandra data to derive meaningful overall uncertainties on the plasma density of the coronae of Capella. We consider two Bayesian frameworks. First, the so-called pragmatic-Bayesian approach considers the a…

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
Hualin Zhan, Viqar Ahmad, Azul Mayon, Grace Tabi, Anh Dinh Bui, Zhuofeng Li, Daniel Walters, Hieu Nguyen, Klaus Weber, Thomas White, Kylie Catchpole
https://arxiv.org/abs/2402.11101

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experi…

Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
Amaury Gouverneur, Borja Rodr\'iguez-G\'alvez, Tobias J. Oechtering, Mikael Skoglund
https://arxiv.org/abs/2403.03361

Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems. It builds upon the information-theoretic framework of [Russo and Van Roy, 2015] and, more specifically, on the rate-distortion analysis from [Dong and Van Roy, 2020], where they proved a bound with regret rate of $O(d\sqrt{T \log(T)})$ for the $d$-dimensional linear bandit setting. We focus on bandit problems with a metric action space and, using a chaining argument, we establish new boun…

Cosmic Inflation at the Crossroads
Jerome Martin, Christophe Ringeval, Vincent Vennin
https://arxiv.org/abs/2404.10647 https://arxiv.…

Cosmic Inflation at the Crossroads
The capability of Cosmic Inflation to explain the latest Cosmic Microwave Background and Baryonic Acoustic Oscillation data is assessed by performing Bayesian model comparison within the landscape of nearly three-hundred models of single-field slow-roll inflation. We present the first Bayesian data analysis based on the third-order slow-roll primordial power spectra. In particular, the fourth Hubble-flow function $ε_4$ remains unbounded while the third function verifies, at two-sigma, $ε_{3}\…

On Bayesian Filtering for Markov Regime Switching Models
Nigar Hashimzade, Oleg Kirsanov, Tatiana Kirsanova, Junior Maih
https://arxiv.org/abs/2402.08051 h…

On Bayesian Filtering for Markov Regime Switching Models
This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian filtering, with a specific focus on the state-space formulation of Dynamic Stochastic General Equilibrium (DSGE) models with multiple regimes. We outline the theoretical foundations of model estimation, provide the details of two families of powerful multiple-regime filters, IMM and GPB, and construct corresponding multiple-regime smoothers. A simulation exercise, based on a prototypical New Ke…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
Maxime HerbrichIPHC, Eythan CousinIPHC, Ivan Puga-GonzalezIPHC, Barbara TiddiIPHC, Claudia FichtelIPHC, Meg CrofootIPHC, Andrew Jj MacintoshIPHC, Erica van de WaalIPHC, C\'edric SueurIPHC
https://arxiv.org/abs/2402.13658…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
This study investigates the prevalence and implications of nestedness within primate social networks, examining its relationship with cognitive and structural factors. We analysed data from 51 primate groups across 21 species, employing network analysis to evaluate nestedness and its correlation with modularity, neocortex ratio, and group size. We used Bayesian mixed effects modelling to investigate nestedness in primate social networks, controlling for phylogenetic dependencies and exploring v…

This https://arxiv.org/abs/1809.04347 has been replaced.
link: https://scholar.google.com/scholar?q=a

High-dimensional Bayesian Fourier Analysis For Detecting Circadian Gene Expressions
In genomic applications, there is often interest in identifying genes whose time-course expression trajectories exhibit periodic oscillations with a period of approximately 24 hours. Such genes are usually referred to as circadian, and their identification is a crucial step toward discovering physiological processes that are clock-controlled. It is natural to expect that the expression of gene i at time j might depend to some degree on the expression of the other genes measured at the same time…

This https://arxiv.org/abs/2311.03556 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Bounds on tidal charges from gravitational-wave ringdown observations
Black hole solutions in the braneworld scenario are predicted to possess a tidal charge parameter, leaving imprints in the quasinormal spectrum. We conduct an extensive computation of such spectrum, and use it to construct a waveform model for the ringdown relaxation regime of binary black hole mergers observed by LIGO and Virgo. Applying a Bayesian time-domain analysis formalism, we analyse a selected dataset from the GWTC-3 LIGO-Virgo-Kagra catalog of binary coalescences, bounding the value o…

This https://arxiv.org/abs/2303.12485 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Flavor hierarchy of parton energy loss in quark-gluon plasma from a Bayesian analysis
The quenching of light and heavy flavor hadrons in relativistic heavy-ion collisions probes the color and flavor dependences of parton energy loss through a color-deconfined quark-gluon plasma (QGP), and thus reveals the properties of QCD matter at extremely high density and temperature. By combining a next-to-leading order perturbative QCD calculation of parton production, a general ansatz of parton energy loss functions and parton fragmentation functions, we calculate the nuclear modification…

This https://arxiv.org/abs/2402.11101 has been replaced.
initial toot: https://mastoxiv.page/@a…

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experi…

A Bayesian shrinkage estimator for transfer learning
Mohamed A. Abba, Jonathan P. Williams, Brian J. Reich
https://arxiv.org/abs/2403.17321 https://…

A Bayesian shrinkage estimator for transfer learning
Transfer learning (TL) has emerged as a powerful tool to supplement data collected for a target task with data collected for a related source task. The Bayesian framework is natural for TL because information from the source data can be incorporated in the prior distribution for the target data analysis. In this paper, we propose and study Bayesian TL methods for the normal-means problem and multiple linear regression. We propose two classes of prior distributions. The first class assumes the d…

Large parameter asymptotic analysis for homogeneous normalized random measures with independent increments
Junxi Zhang, Shui Feng, Yaozhong Hu
https://arxiv.org/abs/2403.14032

Large parameter asymptotic analysis for homogeneous normalized random measures with independent increments
Homogeneous normalized random measures with independent increments (hNRMIs) represent a broad class of Bayesian nonparametric priors and thus are widely used. In this paper, we obtain the strong law of large numbers, the central limit theorem and the functional central limit theorem of hNRMIs when the concentration parameter $a$ approaches infinity. To quantify the convergence rate of the obtained central limit theorem, we further study the Berry-Esseen bound, which turns out to be of the form …

Statistical estimation of full-sky radio maps from 21cm array visibility data using Gaussian Constrained Realisations
Katrine A. Glasscock, Philip Bull, Jacob Burba, Hugh Garsden, Michael J. Wilensky
https://arxiv.org/abs/2403.13766

Statistical estimation of full-sky radio maps from 21cm array visibility data using Gaussian Constrained Realisations
An important application of next-generation wide-field radio interferometers is making high dynamic range maps of radio emission. Traditional deconvolution methods like CLEAN can give poor recovery of diffuse structure, prompting the development of wide-field alternatives like Direct Optimal Mapping and $m$-mode analysis. In this paper, we propose an alternative Bayesian method to infer the coefficients of a full-sky spherical harmonic basis for a drift-scan telescope with potentially thousands…

Cosmic Inflation at the Crossroads
Jerome Martin, Christophe Ringeval, Vincent Vennin
https://arxiv.org/abs/2404.10647 https://arxiv.…

Cosmic Inflation at the Crossroads
The capability of Cosmic Inflation to explain the latest Cosmic Microwave Background and Baryonic Acoustic Oscillation data is assessed by performing Bayesian model comparison within the landscape of nearly three-hundred models of single-field slow-roll inflation. We present the first Bayesian data analysis based on the third-order slow-roll primordial power spectra. In particular, the fourth Hubble-flow function $ε_4$ remains unbounded while the third function verifies, at two-sigma, $ε_{3}\…

This https://arxiv.org/abs/2209.06704 has been replaced.
link: https://scholar.google.com/scholar?q=a

Causal chain event graphs for remedial maintenance
The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead of conventional graphical tools, we apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system. The CEG is derived from an event tree and can flexibly represent the unfolding of asymmetric processes. For this application we need to define a new class of formal interventio…

Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
Amaury Gouverneur, Borja Rodr\'iguez-G\'alvez, Tobias J. Oechtering, Mikael Skoglund
https://arxiv.org/abs/2403.03361

Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems. It builds upon the information-theoretic framework of [Russo and Van Roy, 2015] and, more specifically, on the rate-distortion analysis from [Dong and Van Roy, 2020], where they proved a bound with regret rate of $O(d\sqrt{T \log(T)})$ for the $d$-dimensional linear bandit setting. We focus on bandit problems with a metric action space and, using a chaining argument, we establish new boun…

This https://arxiv.org/abs/2303.14881 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Flavor dependence of jet quenching in heavy-ion collisions from a Bayesian analysis
We investigate the flavor dependence of jet quenching, by performing a systematic analysis of medium modifications on the inclusive jet, $γ$+jet, and $b$-jet in Pb+Pb collisions at the LHC. Our results from MadGraph+PYTHIA exhibit excellent agreement with experimental measurements of the inclusive jet, $γ$+jet and $b$-jet simultaneously in p+p collisions. We then utilize a Bayesian data-driven method to extract systematically the flavor-dependent jet energy loss distributions from experimenta…

This https://arxiv.org/abs/2209.06704 has been replaced.
link: https://scholar.google.com/scholar?q=a

Causal chain event graphs for remedial maintenance
The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead of conventional graphical tools, we apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system. The CEG is derived from an event tree and can flexibly represent the unfolding of asymmetric processes. For this application we need to define a new class of formal interventio…

This https://arxiv.org/abs/2311.12117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Calibrating signal-to-noise ratio detection thresholds using gravitational-wave catalogs
Searching for gravitational-wave signals is a challenging and computationally intensive endeavor undertaken by multiple independent analysis pipelines. While detection depends only on observed noisy data, it is sometimes inconsistently defined in terms of source parameters that in reality are unknown, e.g., by placing a threshold on the optimal signal-to-noise ratio (SNR). We present a method to calibrate unphysical thresholds to search results by performing Bayesian inference on real observati…

This https://arxiv.org/abs/2305.15754 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Bayesian Analysis for Over-parameterized Linear Model without Sparsity
In the field of high-dimensional Bayesian statistics, a plethora of methodologies have been developed, including various prior distributions that result in parameter sparsity. However, such priors exhibit limitations in handling the spectral eigenvector structure of data, rendering estimations less effective for analyzing the over-parameterized models (high-dimensional linear models that do not assume sparsity) developed in recent years. This study introduces a Bayesian approach that employs a …

kDGLM: a R package for Bayesian analysis of Generalized Dynamic Linear Models
Silvaneo V. dos Santos Jr., Mariane Branco Alves, Helio S. Migon
https://arxiv.org/abs/2403.13069

kDGLM: a R package for Bayesian analysis of Generalized Dynamic Linear Models
This paper introduces kDGLM, an R package designed for Bayesian analysis of Generalized Dynamic Linear Models (GDLM), with a primary focus on both uni- and multivariate exponential families. Emphasizing sequential inference for time series data, the kDGLM package provides comprehensive support for fitting, smoothing, monitoring, and feed-forward interventions. The methodology employed by kDGLM, as proposed in Alves et al. (2024), seamlessly integrates with well-established techniques from the l…

This https://arxiv.org/abs/2307.06320 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Bayesian analysis of a Unified Dark Matter model with transition: can it alleviate the $H_{0}$ tension?
We consider cosmological models in which Dark Matter (DM) and Dark Energy (DE) are described by a single component, dubbed Unified Dark Matter (UDM) models, in which the DE-like part can have an equation state $<-1$ at late times without violating the null energy condition. In this paper, we investigate whether this feature can relieve the Hubble tension. We perform a Bayesian analysis of the model using SNIa data from Pantheon, the CMB distance prior from Planck, and the prior on the absolute …

This https://arxiv.org/abs/2309.16865 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

A Bayesian study of quark models in view of recent astrophysical constraints
In this work, we perform a comparative analysis between the density-dependent quark model and the vector MIT bag model using Bayesian analysis. We use the equations of state generated by these two models to describe quark stars. We impose four recent observational astrophysical constraints on both models to determine their model-dependent parameters in an optimized manner assuming that the compact objects observed are composed entirely of self-bound quarks. The restrictions are aimed at produci…

This https://arxiv.org/abs/2305.15754 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Bayesian Analysis for Over-parameterized Linear Model without Sparsity
In the field of high-dimensional Bayesian statistics, a plethora of methodologies have been developed, including various prior distributions that result in parameter sparsity. However, such priors exhibit limitations in handling the spectral eigenvector structure of data, rendering estimations less effective for analyzing the over-parameterized models (high-dimensional linear models that do not assume sparsity) developed in recent years. This study introduces a Bayesian approach that employs a …

Bayesian Nonparametric Trees for Principal Causal Effects
Chanmin Kim, Corwin Zigler
https://arxiv.org/abs/2403.13256 https://arxiv.o…

Bayesian Nonparametric Trees for Principal Causal Effects
Principal stratification analysis evaluates how causal effects of a treatment on a primary outcome vary across strata of units defined by their treatment effect on some intermediate quantity. This endeavor is substantially challenged when the intermediate variable is continuously scaled and there are infinitely many basic principal strata. We employ a Bayesian nonparametric approach to flexibly evaluate treatment effects across flexibly-modeled principal strata. The approach uses Bayesian Causa…

This https://arxiv.org/abs/2210.06648 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evidence for 3XMM J185246.6+003317 as a massive magnetar with a low magnetic field
3XMM J185246.6+003317 is a transient magnetar located in the vicinity of the supernova remnant Kes\,79. So far, observations have only set upper limits to its surface magnetic field and spindown, and there is no estimate for its mass and radius. Using ray-tracing modelling and Bayesian inference for the analysis of several light curves spanning a period of around three weeks, we have found that it may be one of the most massive neutron stars to date. In addition, our analysis suggests a multipo…

This https://arxiv.org/abs/2212.01900 has been replaced.
link: https://scholar.google.com/scholar?q=a

Bayesian survival analysis with INLA
This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS" (Alvares et al., 2021). In addition, we il…

Systematic Biases in Estimating the Properties of Black Holes Due to Inaccurate Gravitational-Wave Models
Arnab Dhani, Sebastian V\"olkel, Alessandra Buonanno, Hector Estelles, Jonathan Gair, Harald P. Pfeiffer, Lorenzo Pompili, Alexandre Toubiana
https://arxiv.org/abs/2404.05811

Systematic Biases in Estimating the Properties of Black Holes Due to Inaccurate Gravitational-Wave Models
Gravitational-wave (GW) observations of binary black-hole (BBH) coalescences are expected to address outstanding questions in astrophysics, cosmology, and fundamental physics. Realizing the full discovery potential of upcoming LIGO-Virgo-KAGRA (LVK) observing runs and new ground-based facilities hinges on accurate waveform models. Using linear-signal approximation methods and Bayesian analysis, we start to assess our readiness for what lies ahead using two state-of-the-art quasi-circular, spin-…

This https://arxiv.org/abs/2212.01900 has been replaced.
link: https://scholar.google.com/scholar?q=a

Bayesian survival analysis with INLA
This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS" (Alvares et al., 2021). In addition, we il…

A uniformly ergodic Gibbs sampler for Bayesian survival analysis
Benny Ren, Jeffrey Morris, Ian Barnett
https://arxiv.org/abs/2402.15060 https://

This https://arxiv.org/abs/2305.07568 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Bayesian analysis of muon capture on deuteron in chiral effective field theory
We compute the muon capture on deuteron in the doublet hyperfine state for a variety of nuclear interactions and consistent nuclear currents. Our analysis includes a detailed examination of the theoretical uncertainties coming from different sources: the single-nucleon axial form factor, the truncation of the interaction and current chiral expansion, and the model dependence. Moreover, we study the impact of the use of different power counting scheme for the electroweak currents on the truncati…

Compressive Bayesian non-negative matrix factorization for mutational signatures analysis
Alessandro Zito, Jeffrey W. Miller
https://arxiv.org/abs/2404.10974

Compressive Bayesian non-negative matrix factorization for mutational signatures analysis
Non-negative matrix factorization (NMF) is widely used in many applications for dimensionality reduction. Inferring an appropriate number of factors for NMF is a challenging problem, and several approaches based on information criteria or sparsity-inducing priors have been proposed. However, inference in these models is often complicated and computationally challenging. In this paper, we introduce a novel methodology for overfitted Bayesian NMF models using "compressive hyperpriors" that force …

This https://arxiv.org/abs/2402.18965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Induced Gravitational Wave interpretation of PTA data: a complete study for general equation of state
We thoroughly study the induced gravitational wave interpretation of the possible gravitational wave background reported by PTA collaborations, considering the unknown equation of state $w$ of the early universe. We perform a Bayesian analysis of the NANOGrav data using the publicly available \textsc{PTArcade} code together with \textsc{SIGWfast} for the numerical integration of the induced gravitational wave spectrum. We focus on two cases: a monochromatic and a log-normal primordial spectrum …

This https://arxiv.org/abs/2402.04461 has been replaced.
link: https://scholar.google.com/scholar?q=a

On Data Analysis Pipelines and Modular Bayesian Modeling
The most common approach to implementing data analysis pipelines involves obtaining point estimates from the upstream modules and then treating these as known quantities when working with the downstream ones. This approach is straightforward, but it is likely to underestimate the overall uncertainty associated with any final estimates. An alternative approach involves estimating parameters from the modules jointly using a Bayesian hierarchical model, which has the advantage of propagating upstr…

This https://arxiv.org/abs/2312.03257 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Bayesian Functional Analysis for Untargeted Metabolomics Data with Matching Uncertainty and Small Sample Sizes
Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application given its ability to depict the global metabolic pattern in biological samples. However, the data is noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching unc…

A fully Bayesian approach for the imputation and analysis of derived outcome variables with missingness
Harlan Campbell, Tim Morris, Paul Gustafson
https://arxiv.org/abs/2404.09966

A fully Bayesian approach for the imputation and analysis of derived outcome variables with missingness
Derived variables are variables that are constructed from one or more source variables through established mathematical operations or algorithms. For example, body mass index (BMI) is a derived variable constructed from two source variables: weight and height. When using a derived variable as the outcome in a statistical model, complications arise when some of the source variables have missing values. In this paper, we propose how one can define a single fully Bayesian model to simultaneously i…

This https://arxiv.org/abs/2305.07568 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Bayesian analysis of muon capture on deuteron in chiral effective field theory
We compute the muon capture on deuteron in the doublet hyperfine state for a variety of nuclear interactions and consistent nuclear currents. Our analysis includes a detailed examination of the theoretical uncertainties coming from different sources: the single-nucleon axial form factor, the truncation of the interaction and current chiral expansion, and the model dependence. Moreover, we study the impact of the use of different power counting scheme for the electroweak currents on the truncati…

Disentangling the Timescales of a Complex System: A Bayesian Approach to Temporal Network Analysis
Giona Casiraghi, Georges Andres
https://arxiv.org/abs/2403.05343

Disentangling the Timescales of a Complex System: A Bayesian Approach to Temporal Network Analysis
Changes in the timescales at which complex systems evolve are essential to predicting critical transitions and catastrophic failures. Disentangling the timescales of the dynamics governing complex systems remains a key challenge. With this study, we introduce an integrated Bayesian framework based on temporal network models to address this challenge. We focus on two methodologies: change point detection for identifying shifts in system dynamics, and a spectrum analysis for inferring the distrib…

This https://arxiv.org/abs/2402.00686 has been replaced.
link: https://scholar.google.com/scholar?q=a

Maximum a posteriori testing in statistical inverse problems
This paper is concerned with a Bayesian approach to testing hypotheses in statistical inverse problems. Based on the posterior distribution $Π\left(\cdot |Y = y\right)$, we want to infer whether a feature $\langleφ, u^\dagger\rangle$ of the unknown quantity of interest $u^\dagger$ is positive. This can be done by the so-called maximum a posteriori test. We provide a frequentistic analysis of this test's properties such as level and power, and prove that it is a regularized test in the se…

This https://arxiv.org/abs/2305.13188 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings
Factors models are routinely used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Carlo (MCMC) methods which scale poorly as the number of studies, observations, or measured variables increase. To address this issue, we propose variational inference algorithms to approximate the posterior distribution of Bayesian latent factor models using the multiplicative gamma process shrinkage prior. The propose…

Primordial Black Hole Interpretation in Subsolar Mass Gravitational Wave Candidate SSM200308
Chen Yuan, Qing-Guo Huang
https://arxiv.org/abs/2404.03328 htt…

Primordial Black Hole Interpretation in Subsolar Mass Gravitational Wave Candidate SSM200308
In the recent second part of the third observation run by the LIGO-Virgo-KAGRA collaboration, a candidate with sub-solar mass components was reported, which we labelled as SSM200308. This study investigates the premise that primordial black holes (PBHs), arising from Gaussian perturbation collapses, could explain SSM200308. Through Bayesian analysis, we obtain the primordial curvature power spectrum that leads to the merger rate of PBHs aligning with observational data as long as they constitut…

A Bayesian factor analysis model for high-dimensional microbiome count data
Isma\"ila Ba, Maxime Turgeon, Simona Veniamin, Juan Joel, Richard Miller, Morag Graham, Christine Bonner, Charles N. Bernstein, Douglas L. Arnold, Amit Bar-Or, Ruth Ann Marrie, Julia O'Mahony, E. Ann Yeh, Brenda Banwell, Emmanuelle Waubant, Natalie Knox, Gary Van Domselaar, Ali I. Mirza, Heather Armstrong, Saman Muthukumarana, Kevin McGregor

A Bayesian factor analysis model for high-dimensional microbiome count data
Dimension reduction techniques are among the most essential analytical tools in the analysis of high-dimensional data. Generalized principal component analysis (PCA) is an extension to standard PCA that has been widely used to identify low-dimensional features in high-dimensional discrete data, such as binary, multi-category and count data. For microbiome count data in particular, the multinomial PCA is a natural counterpart of the standard PCA. However, this technique fails to account for the …

Primordial Black Hole Interpretation in Subsolar Mass Gravitational Wave Candidate SSM200308
Chen Yuan, Qing-Guo Huang
https://arxiv.org/abs/2404.03328 htt…

Primordial Black Hole Interpretation in Subsolar Mass Gravitational Wave Candidate SSM200308
In the recent second part of the third observation run by the LIGO-Virgo-KAGRA collaboration, a candidate with sub-solar mass components was reported, which we labelled as SSM200308. This study investigates the premise that primordial black holes (PBHs), arising from Gaussian perturbation collapses, could explain SSM200308. Through Bayesian analysis, we obtain the primordial curvature power spectrum that leads to the merger rate of PBHs aligning with observational data as long as they constitut…

This https://arxiv.org/abs/2011.09437 has been replaced.
link: https://scholar.google.com/scholar?q=a

Trend and Variance Adaptive Bayesian Changepoint Analysis & Local Outlier Scoring
We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We utilize a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to track noise at eac…

This https://arxiv.org/abs/2307.04063 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

Symmetry energy and neutron star properties constrained by chiral effective field theory calculations
We investigate the nuclear symmetry energy and neutron star properties using a Bayesian analysis based on constraints from different chiral effective field theory calculations using new energy density functionals that allow for large variations at high densities. Constraints at high densities are included from observations of GW170817 and from NICER. In particular, we show that both NICER analyses lead to very similar posterior results for the symmetry energy and neutron star properties when fo…

This https://arxiv.org/abs/2307.07898 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Graph-Prediction-Based Approach for Debiasing Underreported Data
We present a novel Graph-based debiasing Algorithm for Underreported Data (GRAUD) aiming at an efficient joint estimation of event counts and discovery probabilities across spatial or graphical structures. This innovative method provides a solution to problems seen in fields such as policing data and COVID-$19$ data analysis. Our approach avoids the need for strong priors typically associated with Bayesian frameworks. By leveraging the graph structures on unknown variables $n$ and $p$, our meth…

BayesPPDSurv: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Time-To-Event Data
Yueqi Shen, Matthew A. Psioda, Joseph G. Ibrahim
https://arxiv.org/abs/2404.05118

BayesPPDSurv: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Time-To-Event Data
The BayesPPDSurv (Bayesian Power Prior Design for Survival Data) R package supports Bayesian power and type I error calculations and model fitting using the power and normalized power priors incorporating historical data with for the analysis of time-to-event outcomes. The package implements the stratified proportional hazards regression model with piecewise constant hazard within each stratum. The package allows the historical data to inform the treatment effect parameter, parameter effects fo…

Multivariate Bayesian models with flexible shared interactions for analyzing spatio-temporal patterns of rare cancers
Garazi Retegui, Jaione Etxeberria, Mar\'ia Dolores Ugarte
https://arxiv.org/abs/2403.10440

Multivariate Bayesian models with flexible shared interactions for analyzing spatio-temporal patterns of rare cancers
Rare cancers affect millions of people worldwide each year. However, estimating incidence or mortality rates associated with rare cancers presents important difficulties and poses new statistical methodological challenges. In this paper, we expand the collection of multivariate spatio-temporal models by introducing adaptable shared interactions to enable a comprehensive analysis of both incidence and cancer mortality in rare cancer cases. These models allow the modulation of spatio-temporal int…

Astrophysics and Nuclear Physics Informed Interactions in Dense Matter: Insights from PSR J0437-4715
Tuhin Malik, V. Dexheimer, Constan\c{c}a Provid\^encia
https://arxiv.org/abs/2404.07936

Astrophysics and Nuclear Physics Informed Interactions in Dense Matter: Insights from PSR J0437-4715
We investigate how vector and isovector interactions can be determined within the density regime of neutron stars, while fulfilling nuclear and astrophysics constrains. We make use of the Chiral Mean Field (CMF) model, a SU(3) nonlinear realization of the sigma model within the mean-field approximation, for the first time within a Bayesian analysis framework. We show that neutron-matter $χ$EFT constraints at low density are only satisfied if the vector-isovector mixed interaction term is inclu…

A new way to evaluate G-Wishart normalising constants via Fourier analysis
Ching Wong, Giusi Moffa, Jack Kuipers
https://arxiv.org/abs/2404.06803 https://<…

A new way to evaluate G-Wishart normalising constants via Fourier analysis
The G-Wishart distribution is an essential component for the Bayesian analysis of Gaussian graphical models as the conjugate prior for the precision matrix. Evaluating the marginal likelihood of such models usually requires computing high-dimensional integrals to determine the G-Wishart normalising constant. Closed-form results are known for decomposable or chordal graphs, while an explicit representation as a formal series expansion has been derived recently for general graphs. The nested infi…

Blessing of dimension in Bayesian inference on covariance matrices
Shounak Chattopadhyay, Anru R. Zhang, David B. Dunson
https://arxiv.org/abs/2404.03805 h…

Blessing of dimension in Bayesian inference on covariance matrices
Bayesian factor analysis is routinely used for dimensionality reduction in modeling of high-dimensional covariance matrices. Factor analytic decompositions express the covariance as a sum of a low rank and diagonal matrix. In practice, Gibbs sampling algorithms are typically used for posterior computation, alternating between updating the latent factors, loadings, and residual variances. In this article, we exploit a blessing of dimensionality to develop a provably accurate pseudo-posterior for…

This https://arxiv.org/abs/2402.17915 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generation and analysis of synthetic data via Bayesian networks: a robust approach for uncertainty quantification via Bayesian paradigm
Safe and reliable disclosure of information from confidential data is a challenging statistical problem. A common approach considers the generation of synthetic data, to be disclosed instead of the original data. Efficient approaches ought to deal with the trade-off between reliability and confidentiality of the released data. Ultimately, the aim is to be able to reproduce as accurately as possible statistical analysis of the original data using the synthetic one. Bayesian networks is a model-b…

This https://arxiv.org/abs/2312.17061 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Bayesian Analysis of High Dimensional Vector Error Correction Model
Vector Error Correction Model (VECM) is a classic method to analyse cointegration relationships amongst multivariate non-stationary time series. In this paper, we focus on high dimensional setting and seek for sample-size-efficient methodology to determine the level of cointegration. Our investigation centres at a Bayesian approach to analyse the cointegration matrix, henceforth determining the cointegration rank. We design two algorithms and implement them on simulated examples, yielding promi…

The role of the likelihood for elastic scattering uncertainty quantification
C. D. Pruitt, A. E. Lovell, C. Hebborn, F. M. Nunes
https://arxiv.org/abs/2403.00753

The role of the likelihood for elastic scattering uncertainty quantification
Background: Analyses of elastic scattering with the optical model (OMP) are widely used in nuclear reactions. Purpose: Previous work compared a traditional frequentist approach and a Bayesian approach to quantify uncertainties in the OMP. In this study, we revisit this comparison and consider the role of the likelihood used in the analysis. Method: We compare the Levenberg-Marquardt algorithm for $χ^{2}$ minimization with Markov Chain Monte Carlo sampling to obtain parameter posteriors. Fo…

This https://arxiv.org/abs/2402.16520 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Sequential design for surrogate modeling in Bayesian inverse problems
Sequential design is a highly active field of research in active learning which provides a general framework for the design of computer experiments to make the most of a low computational budget. It has been widely used to generate efficient surrogate models able to replace complex computer codes, most notably for uncertainty quantification, Bayesian optimization, reliability analysis or model calibration tasks. In this work, a sequential design strategy is developed for Bayesian inverse proble…

fastcpd: Fast Change Point Detection in R
Xingchi Li, Xianyang Zhang
https://arxiv.org/abs/2404.05933 https://arxiv.org/pdf/2404.0593…

fastcpd: Fast Change Point Detection in R
Change point analysis is concerned with detecting and locating structure breaks in the underlying model of a sequence of observations ordered by time, space or other variables. A widely adopted approach for change point analysis is to minimize an objective function with a penalty term on the number of change points. This framework includes several well-established procedures, such as the penalized log-likelihood using the (modified) Bayesian information criterion (BIC) or the minimum descriptio…

This https://arxiv.org/abs/2309.06988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A basket trial design based on power priors
In basket trials a treatment is investigated in several subgroups. They are primarily used in oncology in early clinical phases as single-arm trials with a binary endpoint. For their analysis primarily Bayesian methods have been suggested, as they allow partial sharing of information based on the observed similarity between subgroups. Fujikawa et al. (2020) suggested an approach using empirical Bayes methods that allows flexible sharing based on easily interpretable weights derived from the Jen…

This https://arxiv.org/abs/2302.09211 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Bayesian Covariance Estimation for Multi-group Matrix-variate Data
Multi-group covariance estimation for matrix-variate data with small within group sample sizes is a key part of many data analysis tasks in modern applications. To obtain accurate group-specific covariance estimates, shrinkage estimation methods which shrink an unstructured, group-specific covariance either across groups towards a pooled covariance or within each group towards a Kronecker structure have been developed. However, in many applications, it is unclear which approach will result in m…