68 citations · 353 across the 57 of their papers we have counts for
11 papers · 1 filter
Approximate Laplace approximations for scalable model selection
David Rossell, Oriol Abril, Anirban Bhattacharya
We propose the approximate Laplace approximation (ALA) to evaluate integrated likelihoods, a bottleneck in Bayesian model selection. The Laplace approximation (LA) is a popular too…
Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors
Niloy Biswas, Anirban Bhattacharya, Pierre E. Jacob +1
We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the ch…
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
Sean Plummer, Shuang Zhou, Anirban Bhattacharya +2
Transformation-based methods have been an attractive approach in non-parametric inference for problems such as unconditional and conditional density estimation due to their unique…
Statistical optimality and stability of tangent transform algorithms in logit models
Indrajit Ghosh, Anirban Bhattacharya, Debdeep Pati
A systematic approach to finding variational approximation in an otherwise intractable non-conjugate model is to exploit the general principle of convex duality by minorizing the m…
Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting
Biraj Subhra Guha, Anirban Bhattacharya, Debdeep Pati
We provide statistical guarantees for Bayesian variational boosting by proposing a novel small bandwidth Gaussian mixture variational family. We employ a functional version of Fran…
Evidence bounds in singular models: probabilistic and variational perspectives
Anirban Bhattacharya, Debdeep Pati, Sean Plummer
The marginal likelihood or evidence in Bayesian statistics contains an intrinsic penalty for larger model sizes and is a fundamental quantity in Bayesian model comparison. Over the…
Dynamics of coordinate ascent variational inference: A case study in 2D Ising models
Sean Plummer, Debdeep Pati, Anirban Bhattacharya
Variational algorithms have gained prominence over the past two decades as a scalable computational environment for Bayesian inference. In this article, we explore tools from the d…
Radius and equation of state constraints from massive neutron stars and GW190814
Yeunhwan Lim, Anirban Bhattacharya, Jeremy W. Holt +1
Motivated by the unknown nature of the compact object in the binary merger event GW190814, we study the maximum neutron star mass based on constraints from low…
Nonasymptotic Laplace approximation under model misspecification
Anirban Bhattacharya, Debdeep Pati
We present non-asymptotic two-sided bounds to the log-marginal likelihood in Bayesian inference. The classical Laplace approximation is recovered as the leading term. Our derivatio…
Nonparametric Bayesian Deconvolution of a Symmetric Unimodal Density
Ya Su, Anirban Bhattacharya, Yan Zhang +2
We consider nonparametric measurement error density deconvolution subject to heteroscedastic measurement errors as well as symmetry about zero and shape constraints, in particular…
Mass-shifting phenomenon of truncated multivariate normal priors
Shuang Zhou, Pallavi Ray, Debdeep Pati +1
We show that lower-dimensional marginal densities of dependent zero-mean normal distributions truncated to the positive orthant exhibit a mass-shifting phenomenon. Despite the trun…