The paper introduces a Bayesian framework for small area estimation that jointly handles borrowing information across areas and benchmarking estimates to reliable aggregates via a new family of Benchmarking priors. These priors arise from a benchmark-constrained regularization problem and include a Benchmarking Prior without additional regularization, as well as Single and Multi-View Laplacian Benchmarking Priors that use graph Laplacians built from area similarities based on external covariates. Tailored MCMC algorithms are developed for posterior computation, and the models are evaluated through a data-based simulation and applied to estimate Average Household Size at the municipality level in Colombia in 2025, where graph-based regularization improves performance and Multi-View models yield more precise estimates.
By Jairo F\'uquene-Pati\~no, Brenda Betancourt
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...
By Alexander Dombowsky, David B. Dunson
The Time-partitioning Gaussian Process Mixture Model (TPGPMM) is a Bayesian nonparametric framework that identifies persistent regimes and models nonstationary dependence in time series. It uses an adaptive Pitman–Yor prior on contiguous segmentations combined with regime-specific Gaussian processes, and learns change points and hyperparameters via a split–merge Metropolis-within-Gibbs sampler. Experiments on the CBOE Volatility Index and the 10‑year U.S. Treasury yield recover regime structures aligned with major market and monetary‑policy events, while variants that estimate observation noise improve predictive accuracy but may reduce sparsity or event alignment.
By Guilherme Piantino, Hedibert F. Lopes
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.
By Sanjay Chaudhuri, Teng Yin, Snehashis Chakraborty, Rupsa Roy
arXiv:2607. 26955v1 Announce Type: cross Abstract: Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality.
By Shuo-Chieh Huang, Chien-Ming Chi, Jau-er Chen
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.
By Zerui Tao, Qibin Zhao
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
By Yichen Gu, Yuxuan Song, Weizhou Qian, Yixin Wang, Joshua Welch
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.
By Jaeyeong Lee, Heeyoung Kim
arXiv:2607. 10793v1 Announce Type: new Abstract: Numerical integration is a cornerstone of various scientific computing applications, such as engineering simulations and model evidence computations in probabilistic machine learning.
By Tim Weiland, Toni Karvonen, Philipp Hennig
arXiv:2606. 27286v1 Announce Type: new Abstract: Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making.
By Alina Bazarova, Johann Fredrik Jadebeck, Henrik Zunker, Carolina J. Klett-Tammen, Torben Heinsohn, Wolfgang Wiechert, Katharina Noeh, Stefan Kesselheim
The paper introduces a Bayesian approach to matrix completion that uses a nuclear norm-based prior and addresses the challenge of unknown noise variance by placing a prior on it. It presents the first sampler for this model, providing a non‑asymptotic polynomial‑time guarantee in terms of matrix dimensions and desired accuracy. The method discretizes the noise precision and employs thermodynamic integration to construct a categorical posterior, offering a feasibility result for Bayesian sampling in non‑log‑concave settings.
By Calvin Tolbert
arXiv:2606. 00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning.
By Dahai Yu, Rongchao Xu, Lin Jiang, Guang Wang