7 papers
Orthogonal calibration via posterior projections with applications to the Schwarzschild model
Antik Chakraborty, Jonelle B. Walsh, Louis Strigari +2
The orbital superposition method originally developed by Schwarzschild (1979) is used to study the dynamics of growth of a black hole and its host galaxy, and has uncovered new rel…
Bayesian inference on high-dimensional multivariate binary responses
Antik Chakraborty, Rihui Ou, David B. Dunson
It has become increasingly common to collect high-dimensional binary response data; for example, with the emergence of new sampling techniques in ecology. In smaller dimensions, mu…
Bayesian semiparametric long memory models for discretized event data
Antik Chakraborty, Otso Ovaskainen, David B. Dunson
We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in…
Nearest Neighbor Dirichlet Mixtures
Shounak Chattopadhyay, Antik Chakraborty, David B. Dunson
There is a rich literature on Bayesian methods for density estimation, which characterize the unknown density as a mixture of kernels. Such methods have advantages in terms of prov…
Bayesian sparse multiple regression for simultaneous rank reduction and variable selection
Antik Chakraborty, Anirban Bhattacharya, Bani K. Mallick
We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a…
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression
Anirban Bhattacharya, Antik Chakraborty, Bani K. Mallick
We propose an efficient way to sample from a class of structured multivariate Gaussian distributions which routinely arise as conditional posteriors of model parameters that are as…
Bayesian Semiparametric Multivariate Density Deconvolution
Abhra Sarkar, Debdeep Pati, Bani K. Mallick +1
We consider the problem of multivariate density deconvolution when the interest lies in estimating the distribution of a vector-valued random variable but precise measurements of t…