Frequentist Guarantees of Distributed (Non)-Bayesian Inference

Bohan Wu, César A. Uribe

We establish frequentist properties, i.e., posterior consistency, asymptotic normality, and posterior contraction rates, for the distributed (non-)Bay...

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

Sudipto Banerjee, Xiang Chen, Ian Frankenburg et al.

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the me...

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

Jannis Chemseddine, Paul Hagemann, Gabriele Steidl et al.

In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its l...

Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights

Paul Egels, Ismaël Castillo

We consider deep neural networks in a Bayesian framework with a prior distribution sampling the network weights at random. Following a recent idea of...

Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo

Max Hird, Samuel Livingstone

We study linear preconditioning in Markov chain Monte Carlo. We consider the class of well-conditioned distributions, for which several mixing time bo...

Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior

Keru Wu, Ben Wu, Jian Kang

Deep neural networks (DNN) have been widely used in scalar-on-image regression to predict an outcome variable from imaging predictors. However, train...

Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning

Piyali Basak, Camille Maringe, F. Javier Rubio et al.

A Bayesian nonparametric approach to mediation and spillover effects with multiple mediators in cluster-randomized trials

Yuki Ohnishi, Fan Li

Semiparametric Bernstein-Von Mises Phenomenon via Isotonized Posterior in Wicksell’s Problem

Francesco Gili, Aad van der Vaart, Geurt Jongbloed

Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition

Andrea Montanari, Yuchen Wu

Solving the Poisson Equation Using Coupled Markov Chains

Pierre Etienne Jacob, Randal Douc, Anthony Lee et al.

Poisson Empirical Bayes Estimation: When Doesg-Modeling Beatf-Modeling in Theory (And in Practice)?

Yihong Wu, Yandi Shen

Advances in Bayesian Model Selection Consistency for High-Dimensional Generalized Linear Models

Jeyong Lee, Minwoo Chae, Ryan Martin

Bayesian Data Sketching for Varying Coefficient Regression Models

Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee

Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received ...

Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights

Insung Kong, Yongdai Kim

Bayesian approaches for training deep neural networks (BNNs) have received significant interest and have been effectively utilized in a wide range of ...

Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers

Federico Bassetti, Marco Gherardi, Alessandro Ingrosso et al.

Deep linear networks have been extensively studied, as they provide simplified models of deep learning. However, little is known in the case of finite...

How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences

Miko{\l}aj J. Kasprzak, Ryan Giordano, Tamara Broderick

The Laplace approximation is a popular method for constructing a Gaussian approximation to the Bayesian posterior and thereby approximating the poster...

Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems

Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas

We propose an operator learning approach to accelerate geometric Markov chain Monte Carlo (MCMC) for solving infinite-dimensional Bayesian inverse pro...

On Consistent Bayesian Inference from Synthetic Data

Ossi Räisä, Joonas Jälkö, Antti Honkela

Generating synthetic data, with or without differential privacy, has attracted significant attention as a potential solution to the dilemma between ma...

DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

Xiangdong Xie, Jiahua Guo, Yi Sun

Bayesian networks (BNs) are a powerful tool for knowledge representation and reasoning, especially for complex systems. A critical task in the applic...

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

Shouri Hu, Haowei Wang, Zhongxiang Dai et al.

The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical perf...

Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data

Sudipto Banerjee, Didong Li, Andrew Jones et al.

Gaussian processes are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Scientific d...

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Yanxun Xu, Fangzheng Xie, Dapeng Yao

We study the sparse high-dimensional Gaussian mixture model when the number of clusters is allowed to grow with the sample size. A minimax lower bound...

Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes

Pierre Alquier, Charles Riou, Badr-Eddine Chérief-Abdellatif

Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, under this condition, the Gibbs posterior with ...

Asymptotic Guarantees for Bayesian Phylogenetic Tree Reconstruction

Alisa Kirichenko, Luke J. Kelly, Jere Koskela

Factor pre-training in Bayesian multivariate logistic models

D B Dunson, L Mauri

Abstract

Bayesian Random-Effects Meta-Analysis Integrating Individual Participant Data and Aggregate Data

Yunxiang Huang, Hang J. Kim, Chiung-Yu Huang et al.

Bayesian Inference on Brain-Computer Interfaces via GLASS

Bangyao Zhao, Jane E. Huggins, Jian Kang

A Smoothed-Bayesian Approach to Frequency Recovery from Sketched Data

Mario Beraha, Stefano Favaro, Matteo Sesia

Estimating Heterogeneous Causal Mediation Effects with Bayesian Decision Tree Ensembles

Angela Ting, Antonio R. Linero

Posterior Predictive Design for Phase I Clinical Trials

Chenqi Fu, Shouhao Zhou, J. Jack Lee

A Bayesian Criterion for Rerandomization

Zhaoyang Liu, Tingxuan Han, Donald B. Rubin et al.

Sparse Bayesian Multidimensional Item Response Theory

Jiguang Li, Robert Gibbons, Veronika Ročková

Bayesian mixture models with repulsive and attractive atoms

Mario Beraha, others

Product centred Dirichlet processes for Bayesian multiview clustering

Alexander Dombowsky, David B Dunson

An Economical Approach to Design Posterior Analyses

Luke Hagar, Nathaniel T. Stevens

A general framework for cutting feedback within modularized Bayesian inference

Yang Liu, Robert J B Goudie

A spike-and-slab prior for dimension selection in generalized linear network eigenmodels

Joshua D Loyal, Yuguo Chen

Abstract

Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling

Jinyuan Chang, others

Semiparametric posterior corrections

Andrew Yiu, others

Robust Bayesian Modeling of Counts with Zero Inflation and Outliers: Theoretical Robustness and Efficient Computation

Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa

Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings

Michael Pearce, Elena A. Erosheva

Bayesian Clustering via Fusing of Localized Densities

Alexander Dombowsky, David B. Dunson

Geometric Ergodicity of Trans-Dimensional Markov Chain Monte Carlo Algorithms

Qian Qin