158 citations · 185 across the 9 of their papers we have counts for
11 papers
-PSD: Scalable Approximate SNR-Optimised Polynomial Stein Discrepancies
Minh-Long Nguyen, Thanh-Long Vu, Christopher Drovandi +2
Polynomial Stein discrepancies (PSD) provide a scalable alternative to kernel Stein methods for measuring sample quality and goodness-of-fit testing, but their statistical properti…
Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC; Chopin et al., 2012) methods can be used to sample from the exact posterior distribution of intractable likelihood state space models. Thes…
Nested sampling for physical scientists
Greg Ashton, Noam Bernstein, Johannes Buchner +20
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey deve…
Automatically adapting the number of state particles in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbi…
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC
Jeremie Coullon, Leah South, Christopher Nemeth
Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of algorithms for scalable Bayesian inference. However, these algorithms include hyperparameters such as st…
Post-Processing of MCMC
Leah F. South, Marina Riabiz, Onur Teymur +1
Markov chain Monte Carlo (MCMC) is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of…
Semi-Exact Control Functionals From Sard's Method
Leah F. South, Toni Karvonen, Chris Nemeth +2
The numerical approximation of posterior expected quantities of interest is considered. A novel control variate technique is proposed for post-processing of Markov chain Monte Carl…
Discussion of "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé
Leah F. South, Chris Nemeth, Chris J. Oates
This is a contribution for the discussion on "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé to appear in the Journal of the…
BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood
Ziwen An, Leah F South, Christopher Drovandi
Bayesian synthetic likelihood (BSL) is a popular method for estimating the parameter posterior distribution for complex statistical models and stochastic processes that possess a c…
Regularized Zero-Variance Control Variates
Leah F. South, Chris J. Oates, Antonietta Mira +1
Zero-variance control variates (ZV-CV) are a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once t…
Unbiased and Consistent Nested Sampling via Sequential Monte Carlo
Robert Salomone, Leah F. South, Christopher Drovandi +2
We introduce a new class of sequential Monte Carlo methods which reformulates the essence of the nested sampling method of Skilling (2006) in terms of sequential Monte Carlo techni…