9 citations · 9 across the 8 of their papers we have counts for
9 papers · 1 filter
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…
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 convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
Alain Durmus, Samuel Gruffaz, Miika Kailas +2
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian Monte Carlo (HMC), such as the No U-Turn Sampler (NUTS), in many challenging inf…
Conditional particle filters with diffuse initial distributions
Santeri Karppinen, Matti Vihola
Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to appl…