61 citations · 294 across the 31 of their papers we have counts for
17 papers · 1 filter
An invitation to sequential Monte Carlo samplers
Chenguang Dai, Jeremy Heng, Pierre E. Jacob +1
Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo sample…
Parallelising Particle Filters with Butterfly Interactions
Kari Heine, Nick Whiteley, A. Taylan Cemgil
Bootstrap particle filter (BPF) is the corner stone of many popular algorithms used for solving inference problems involving time series that are observed through noisy measurement…
Global consensus Monte Carlo
Lewis J. Rendell, Adam M. Johansen, Anthony Lee +1
To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribute the data across multiple machines. We consider a likelihood function expressed…
Negative association, ordering and convergence of resampling methods
Mathieu Gerber, Nicolas Chopin, Nick Whiteley
We study convergence and convergence rates for resampling schemes. Our first main result is a general consistency theorem based on the notion of negative association, which is appl…
Stability with respect to initial conditions in V-norm for nonlinear filters with ergodic observations
Mathieu Gerber, Nick Whiteley
We establish conditions for an exponential rate of forgetting of the initial distribution of nonlinear filters in -norm, path-wise along almost all observation sequences. In con…
An Introduction to Twisted Particle Filters and Parameter Estimation in Non-linear State-space Models
Juha Ala-Luhtala, Nick Whiteley, Kari Heine +1
Twisted particle filters are a class of sequential Monte Carlo methods recently introduced by Whiteley and Lee to improve the efficiency of marginal likelihood estimation in state-…