70 citations · 315 across the 30 of their papers we have counts for
35 papers
Adaptive Riemannian Manifold Hamiltonian Monte Carlo with Hierarchical Metric
Miika Kailas, Matti Vihola, Jonas Wallin
Hamiltonian Monte Carlo (HMC) and its dynamic extensions, such as the No-U-Turn Sampler (NUTS), are powerful Markov chain Monte Carlo methods for sampling from complex, high-dimens…
On the complexity of standard and waste-free SMC samplers
Yvann Le Fay, Nicolas Chopin, Matti Vihola
We establish finite sample bounds for the error of standard and waste-free SMC samplers. Our results cover estimates of both expectations and normalising constants of the target di…
Iterated sampling importance resampling with adaptive number of proposals
Pietari Laitinen, Matti Vihola
Iterated sampling importance resampling (i-SIR) is a Markov chain Monte Carlo (MCMC) algorithm which is based on independent proposals. As grows, its samples become nearly…
An invitation to adaptive Markov chain Monte Carlo convergence theory
Pietari Laitinen, Matti Vihola
Adaptive Markov chain Monte Carlo (MCMC) algorithms, which automatically tune their parameters based on past samples, have proved extremely useful in practice. The self-tuning mech…
Mixing time of the conditional backward sampling particle filter
Joona Karjalainen, Anthony Lee, Sumeetpal S. Singh +1
The conditional backward sampling particle filter (CBPF) is a powerful Markov chain Monte Carlo sampler for general state space hidden Markov model (HMM) smoothing. It was proposed…
On the Forgetting of Particle Filters
Joona Karjalainen, Anthony Lee, Sumeetpal S. Singh +1
We study the forgetting properties of the particle filter when its state - the collection of particles - is regarded as a Markov chain. Under a strong mixing assumption on the part…