1 citations · 1 across the 2 of their papers we have counts for
4 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…
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…
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…
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…