5 citations · 14 across the 20 of their papers we have counts for
5 papers · 1 filter
Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning
Minerva Mukhopadhyay, Jacie R. McHaney, Bharath Chandrasekaran +1
Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mi…
Bayesian Semiparametric Hidden Markov Tensor Partition Models for Longitudinal Data with Local Variable Selection
Giorgio Paulon, Peter Müller, Abhra Sarkar
We present a flexible Bayesian semiparametric mixed model for longitudinal data analysis in the presence of potentially high-dimensional categorical covariates. Building on a novel…
Bayesian Scalable Precision Factor Analysis for Massive Sparse Gaussian Graphical Models
Noirrit Kiran Chandra, Peter Mueller, Abhra Sarkar
We propose a novel approach to estimating the precision matrix of multivariate Gaussian data that relies on decomposing them into a low-rank and a diagonal component. Such decompos…
Bayesian Semiparametric Markov Renewal Mixed Models for Vocalization Syntax
Yutong Wu, Erich D. Jarvis, Abhra Sarkar
Studying the neurological, genetic and evolutionary basis of human vocal communication mechanisms is an important field of neuroscience. In the absence of high quality data on huma…
Bayesian Semiparametric Multivariate Density Deconvolution via Stochastic Rotation of Replicates
Arkaprava Roy, Abhra Sarkar
We consider the problem of multivariate density deconvolution where the distribution of a random vector needs to be estimated from replicates contaminated with conditionally hetero…