36 citations · 65 across the 21 of their papers we have counts for
10 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…