arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.
By Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk
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
Product Monte Carlo (PMC) is an algorithm that estimates expectations under a product of densities using only Monte Carlo samples from each individual density. By reformulating the intractable product expectation as a regression problem involving computable conditional expectations, PMC provides both point estimates and coherent Bayesian uncertainty quantification. The method achieves higher accuracy, better-calibrated uncertainty, and improved scalability compared to existing techniques, and it does not require density evaluations, enabling its use in simulation‑based inference such as protein image analysis.
By Pietro Carlotti, Sifan Liu, Yuling Yao
The paper introduces Semi-Linear Gaussian Bayesian Networks (SLGBN), a new class of continuous Bayesian networks that combine linear Gaussian models with nonlinear conditional mean functions estimated via penalized B-splines within generalized additive models. SLGBN preserves Gaussian conditional distributions while allowing nonlinear relationships, and the authors provide parameter estimation procedures and a greedy hill‑climbing algorithm for structure learning. Experiments comparing SLGBN to semiparametric and linear Gaussian models show that SLGBN achieves better goodness‑of‑fit and structure recovery, especially when using the EBIC* score, while maintaining computational efficiency.
By Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz, Ruben Arma\~nanzas
arXiv:2607. 24583v1 Announce Type: new Abstract: Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large training size at fractional cost.
By Kart-Leong Lim
The paper introduces additive tree models for estimating the density ratio between two distributions, using a novel balancing loss that enables training with supervised learning algorithms such as forward‑stagewise optimization and gradient boosting. The loss also functions as a pseudo‑likelihood, allowing generalized Bayesian inference via backfitting samplers for Bayesian additive regression trees, which provides uncertainty quantification. Experiments show that the boosting algorithms achieve high accuracy with low computational cost, and the Bayesian approach offers competitive accuracy while delivering uncertainty estimates, demonstrated on microbiome compositional data.
By Naoki Awaya, Yuliang Xu, Li Ma