Abstract Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to …

To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased
Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational …

Stein discrepancies (SDs) monitor convergence and non-convergence in approximate
inference when exact integration and sampling are intractable. However, the computation of …

We propose a procedure for optimising the friction matrix of underdamped Langevin
dynamics when used for continuous time Markov Chain Monte Carlo. Starting from a central …

Drawing a sample from a discrete distribution is one of the building components for Monte
Carlo methods. Like other sampling algorithms, discrete sampling suffers from the high …

Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and
decoding while Markov chain Monte Carlo (MCMC) methods from statistics offer an effective …

Abstract Scaling Markov Chain Monte Carlo (MCMC) to estimate posterior distributions from
large datasets has been made possible as a result of advances in stochastic gradient …

We consider a pharmaceutical company that sources a biological product from a set of
unreliable contract manufacturers. The likelihood of a manufacturer to successfully deliver …

When dealing with datasets containing a billion instances or with simulations that require a
supercomputer to execute, computational resources become part of the equation. We can …

Traditional algorithms for Bayesian posterior inference require processing the entire dataset
in each iteration and are quickly getting obsoleted by the proliferation of massive datasets in …