6 papers
Robust, partially alive particle Metropolis-Hastings via the Frankenfilter
Chris Sherlock, Andrew Golightly, Anthony Lee
When a hidden Markov model permits the conditional likelihood of an observation given the hidden process to be zero, all particle simulations from one observation time to the next…
Nested ensemble Kalman filter for static parameter inference in nonlinear state-space models
Andrew Golightly, Sarah E. Heaps, Chris Sherlock +2
The ensemble Kalman filter (EnKF) is a popular technique for performing inference in state-space models (SSMs), particularly when the dynamic process is high-dimensional. Unlike re…
Reversible Markov chains: variational representations and ordering
Chris Sherlock
This pedagogical document explains three variational representations that are useful when comparing the efficiencies of reversible Markov chains: (i) the Dirichlet form and the ass…
Centered plug-in estimation of Wasserstein distances
Tamás P. Papp, Chris Sherlock
The plug-in estimator of the squared Euclidean 2-Wasserstein distance is conservative, however due to its large positive bias it is often uninformative. We eliminate most of this b…
Variance bounds and robust tuning for pseudo-marginal Metropolis--Hastings algorithms
Chris Sherlock
The general applicability and ease of use of the pseudo-marginal Metropolis--Hastings (PMMH) algorithm, and particle Metropolis--Hastings in particular, makes it a popular method f…
Scalable couplings for the random walk Metropolis algorithm
Tamás P. Papp, Chris Sherlock
There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo algorithms: they facilitate convergence quantification and unbiased estimation, while exp…