Search Results for author:
Found 7 papers, 2 papers with code
Semiparametric inference using fractional posteriors
We establish a general Bernstein--von Mises theorem for approximately linear semiparametric functionals of fractional posterior distributions based on nonparametric priors.
On the inability of Gaussian process regression to optimally learn compositional functions
We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure.
Variational Bayes for high-dimensional proportional hazards models with applications within gene expression
Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification.
Nonparametric Bayesian inference for reversible multi-dimensional diffusions
We study nonparametric Bayesian models for reversible multi-dimensional diffusions with periodic drift.
Bayesian Inference
Statistics Theory
Numerical Analysis
Numerical Analysis
Probability
Statistics Theory
Spike and slab variational Bayes for high dimensional logistic regression
Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for Bayesian inference.
Debiased Bayesian inference for average treatment effects
Bayesian approaches have become increasingly popular in causal inference problems due to their conceptual simplicity, excellent performance and in-built uncertainty quantification ('posterior credible sets').
Variational Bayes for high-dimensional linear regression with sparse priors
We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression.
