Search Results for author:
Found 6 papers, 0 papers with code
Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency
Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic optimization problems.
PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models
We present a PAC-Bayesian analysis of variational Bayes (VB) approximations to tempered Bayesian posterior distributions, bounding the model risk of the VB approximations.
Statistics Theory Statistics Theory
Variational Bayesian Methods for Stochastically Constrained System Design Problems
We study system design problems stated as parameterized stochastic programs with a chance-constraint set.
Asymptotic Consistency of Loss-Calibrated Variational Bayes
We also establish the asymptotic consistency of decision rules obtained from a `naive' variational Bayesian procedure.
Asymptotic Consistency of $α-$Rényi-Approximate Posteriors
We study the asymptotic consistency properties of $\alpha$-R\'enyi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with a member of a tractable family of distributions, the member chosen to minimize the $\alpha$-R\'enyi divergence from the true posterior.
Collapsed variational Bayes for Markov jump processes
Markov jump processes are continuous-time stochastic processes widely used in statistical applications in the natural sciences, and more recently in machine learning.
