1 citations · 1 across the 5 of their papers we have counts for
5 papers
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.…
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…
Posterior Consistency in Parametric Models via a Tighter Notion of Identifiability
Nicola Bariletto, Bernardo Flores, Stephen G. Walker
We study Bayesian posterior consistency in parametric density models with proper priors, challenging the perception that the problem is settled. Classical results established consi…
Predictive Coresets
Bernardo Flores
Modern data analysis often involves massive datasets with hundreds of thousands of observations, making traditional inference algorithms computationally prohibitive. Coresets are s…
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…