13 citations · 15 across the 4 of their papers we have counts for
5 papers · 1 filter
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data
Yabo Niu, Yang Ni, Debdeep Pati +1
In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there i…
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling
Sutanoy Dasgupta, Peng Zhao, Jacob Helwig +3
Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-li…
A Bayesian Survival Tree Partition Model Using Latent Gaussian Processes
Richard D. Payne, Nilabja Guha, Bani K. Mallick
Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazards a…
Bayesian Structural Equation Modeling in Multiple Omics Data Integration with Application to Circadian Genes
Arnab Kumar Maity, Sang Chan Lee, Bani K. Mallick +1
It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions which might be d…
Nonparametric Bayesian Approaches to Non-homogeneous Hidden Markov Models
Abhra Sarkar, Anindya Bhadra, Bani K. Mallick
In this article a flexible Bayesian non-parametric model is proposed for non-homogeneous hidden Markov models. The model is developed through the amalgamation of the ideas of hidde…