17 citations · 21 across the 9 of their papers we have counts for
10 papers
Stochastic Variance-Reduced Majorization-Minimization Algorithms
Duy-Nhat Phan, Sedi Bartz, Nilabja Guha +1
We study a class of nonconvex nonsmooth optimization problems in which the objective is a sum of two functions: One function is the average of a large number of differentiable func…
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…
On Posterior consistency of Bayesian Changepoint models
Nilabja Guha, Jyotishka Datta
While there have been a lot of recent developments in the context of Bayesian model selection and variable selection for high dimensional linear models, there is not much work in t…
Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
Yabo Niu, Nilabja Guha, Debkumar De +3
Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorpo…
Stochastic Approximation Algorithm for Estimating Mixing Distribution for Dependent Observations
Nilabja Guha, Anindya Roy
Estimating the mixing density of a mixture distribution remains an interesting problem in statistics literature. Using a stochastic approximation method, Newton and Zhang (1999) in…
Dynamic Data-driven Bayesian GMsFEM
Siu Wun Cheung, Nilabja Guha
In this paper, we propose a Bayesian approach for multiscale problems with the availability of dynamic observational data. Our method selects important degrees of freedom probabili…
A Conditional Density Estimation Partition Model Using Logistic Gaussian Processes
Richard D. Payne, Nilabja Guha, Yu Ding +1
Conditional density estimation (density regression) estimates the distribution of a response variable y conditional on covariates x. Utilizing a partition model framework, a condit…
Bayesian Multiscale Finite Element Methods. Modeling missing subgrid information probabilistically
Y. Efendiev, W. T. Leung, S. W. Cheung +3
In this paper, we develop a Bayesian multiscale approach based on a multiscale finite element method. Because of scale disparity in many multiscale applications, computational mode…
Quantile Graphical Models: Bayesian Approaches
Nilabja Guha, Veera Baladandayuthapani, Bani K. Mallick
Graphical models are ubiquitous tools to describe the interdependence between variables measured simultaneously such as large-scale gene or protein expression data. Gaussian graphi…
Bayesian and Variational Bayesian approaches for flows in heterogenous random media
Keren Yang, Nilabja Guha, Yalchin Efendiev +1
In this paper, we study porous media flows in heterogeneous stochastic media. We propose an efficient forward simulation technique that is tailored for variational Bayesian inversi…