arXiv Statistics ML
Graph-dependent shrinkage priors for Bayesian trend filtering
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arXiv Statistics ML
Aug 25
Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
arXiv:2509.13267v3 Announce Type: replace-cross Abstract: A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling in...
arXiv Statistics ML
Aug 28
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Quantile Regression and Bayesian Model Selection
The paper introduces a two-step Metropolis–Hastings algorithm designed to efficiently sample from Bayesian empirical likelihood (BayesEL) posterior distributions, addressing challenges posed by the complex, often non‑convex support of empirical likelihood. The method leverages current parameter values and estimating equations to propose new values for remaining parameters, making it suitable for problems with discontinuous estimating equations such as simultaneous quantile regression. Additionally, the approach extends naturally to BayesEL model selection via reversible‑jump MCMC, and the authors demonstrate its utility through several real‑life applications.
arXiv Machine Learning
Jul 30
Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions
arXiv:2607. 26955v1 Announce Type: cross Abstract: Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality.
arXiv Machine Learning
Jun 3
Bayesian Tensor Decomposition with Diffusion Model Prior
arXiv:2606. 03212v1 Announce Type: new Abstract: Low-rank tensor decomposition (TD) is usually effective on clean, fully observed data, but it often degrades under severe missingness or noise.
arXiv Machine Learning
Jul 28
Amortized Bayesian Causal Discovery of Extended Factor Graphs
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
arXiv Machine Learning
Jun 15
Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamics
arXiv:2606. 14313v1 Announce Type: cross Abstract: Real-world spatio-temporal forecasting must handle irregular time points, spatially sparse observations, and the need for uncertainty quantification.