3 papers
stat.ME2024
Dirichlet process mixtures of block priors for model selection and prediction in linear models
Anupreet Porwal, Abel Rodriguez
This paper introduces Dirichlet process mixtures of block priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of …
stat.ME2021
Laplace Power-expected-posterior priors for generalized linear models with applications to logistic regression
Anupreet Porwal, Abel Rodriguez
Power-expected-posterior (PEP) methodology, which borrows ideas from the literature on power priors, expected-posterior priors and unit information priors, provides a systematic wa…
stat.ML2018
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels
Gundeep Arora, Anupreet Porwal, Kanupriya Agarwal +2
The latent feature relational model (LFRM) is a generative model for graph-structured data to learn a binary vector representation for each node in the graph. The binary vector den…