1 citations · 2 across the 5 of their papers we have counts for
6 papers
Bayesian learning for the stochastic shortest path problem
Chon Wai Ho, Sumeetpal S. Singh, Jiaqi Guo
Sequential decision-making problems are often modelled as a Markov decision process (MDP). We focus on the stochastic shortest path (SSP) problem, which is an infinite-horizon undi…
Bayesian learning of the optimal action-value function in a Markov decision process
Jiaqi Guo, Chon Wai Ho, Sumeetpal S. Singh
The Markov Decision Process (MDP) is a popular framework for sequential decision-making problems, and uncertainty quantification is an essential component of it to learn optimal de…
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…
Conditional particle filters with bridge backward sampling
Santeri Karppinen, Sumeetpal S. Singh, Matti Vihola
Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such a…
Blocking Strategies and Stability of Particle Gibbs Samplers
Sumeetpal S. Singh, Fredrik Lindsten, Eric Moulines
Sampling from the conditional (or posterior) probability distribution of the latent states of a Hidden Markov Model, given the realization of the observed process, is a non-trivial…