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stat.ME2026
Newton's Algorithm as a Gradient Flow: A Geometric Framework for Recursive Mixture Estimation
Bernardo Flores
Bayesian nonparametric mixture models provide a flexible framework for data analysis but are often hindered by the computational expense of traditional inference methods like MCMC.…
stat.ME2025
A Dependent Feature Allocation Model Based on Random Fields
Bernardo Flores, Yang Ni, Yanxun Xu +1
We introduce a flexible framework for modeling dependent feature allocations. Our approach addresses limitations in traditional nonparametric methods by directly modeling the logit…
stat.ME2024★ 1 cited
Clustering and Meta-Analysis Using a Mixture of Dependent Linear Tail-Free Priors
Bernardo Flores, Peter Mueller
We propose a novel nonparametric Bayesian approach for meta-analysis with event time outcomes. The model is an extension of linear dependent tail-free processes. The extension incl…