Jan 10, 2013 · Abstract:We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) ...

Dec 7, 2022 · MCMC is a sampling method. It's an exceedingly clever algorithm for sampling from the distribution of latent (unobserved) model parameters.

We propose a new class of learning algorithms that combines variational approximation and. Markov chain Monte Carlo (MCMC) simu~ lation.

By doing so we obtain a rich class of inference algorithms bridging the gap between variational methods and MCMC, and offering the best of both worlds: fast ...

In computational physics, variational Monte Carlo (VMC) is a quantum Monte Carlo method that applies the variational method to approximate the ground state ...

In this paper, we propose variational consensus Monte Carlo (VCMC), a novel class of data-parallel. MCMC algorithms that allow both questions to be addressed.

We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simulation.

A new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simulation and demonstrates the algorithms on ...

Abstract. Practitioners of Bayesian statistics have long depended on Markov chain Monte Carlo (MCMC) to obtain samples from intractable posterior distributions.

Oct 23, 2014 · This enables us to explore a new synthesis of variational inference and Monte Carlo methods where we incorporate one or more steps of MCMC into ...