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Found 4 papers, 2 papers with code
Variational Rejection Particle Filtering
We present a variational inference (VI) framework that unifies and leverages sequential Monte-Carlo (particle filtering) with \emph{approximate} rejection sampling to construct a flexible family of variational distributions.
Partial Rejection Control for Robust Variational Inference in Sequential Latent Variable Models
Effective variational inference crucially depends on a flexible variational family of distributions.
Estimating Monte Carlo variance from multiple Markov chains
We demonstrate that simply averaging covariance matrix estimators from multiple chains (average BM) can yield critical underestimates in small sample sizes, especially for slow mixing Markov chains.
Methodology Computation
Multivariate Output Analysis for Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) produces a correlated sample for estimating expectations with respect to a target distribution.
Statistics Theory Computation Statistics Theory
