27 citations · 81 across the 21 of their papers we have counts for
11 papers · 1 filter
Transient regime of piecewise deterministic Monte Carlo algorithms
Sanket Agrawal, Joris Bierkens, Kengo Kamatani +1
Piecewise Deterministic Markov Processes (PDMPs) such as the Bouncy Particle Sampler and the Zig-Zag Sampler, have gained attention as continuous-time counterparts of classical Mar…
Automated Techniques for Efficient Sampling of Piecewise-Deterministic Markov Processes
Charly Andral, Kengo Kamatani
Piecewise deterministic Markov processes (PDMPs) are a class of continuous-time Markov processes that were recently used to develop a new class of Markov chain Monte Carlo algorith…
Multilevel Particle Filters for a Class of Partially Observed Piecewise Deterministic Markov Processes
Ajay Jasra, Kengo Kamatani, Mohamed Maama
In this paper we consider the filtering of a class of partially observed piecewise deterministic Markov processes (PDMPs). In particular, we assume that an ordinary differential eq…
MCMC Algorithms for Posteriors on Matrix Spaces
Alexandros Beskos, Kengo Kamatani
We study Markov chain Monte Carlo (MCMC) algorithms for target distributions defined on matrix spaces. Such an important sampling problem has yet to be analytically explored. We ca…
The Boomerang Sampler
Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani +1
This paper introduces the Boomerang Sampler as a novel class of continuous-time non-reversible Markov chain Monte Carlo algorithms. The methodology begins by representing the targe…
Non-reversible guided Metropolis kernel
Kengo Kamatani, Xiaolin Song
We construct a class of non-reversible Metropolis kernels as a multivariate extension of the guided-walk kernel proposed by Gustafson 1998. The main idea of our method is to introd…
A Multi-Index Markov Chain Monte Carlo Method
Ajay Jasra, Kengo Kamatani, Kody Law +1
In this article we consider computing expectations w.r.t.~probability laws associated to a certain class of stochastic systems. In order to achieve such a task, one must not only r…
Bayesian Static Parameter Estimation for Partially Observed Diffusions via Multilevel Monte Carlo
Ajay Jasra, Kengo Kamatani, Kody J. H. Law +1
In this article we consider static Bayesian parameter estimation for partially observed diffusions that are discretely observed. We work under the assumption that one must resort t…
Multilevel Particle Filters: Normalizing Constant Estimation
Ajay Jasra, Kengo Kamatani, Prince Prepah Osei +1
In this article we introduce two new estimates of the normalizing constant (or marginal likelihood) for partially observed diffusion (POD) processes, with discrete observations. On…
Multilevel particle filter
Ajay Jasra, Kengo Kamatani, Kody J. H. Law +1
In this paper the filtering of partially observed diffusions, with discrete-time observations, is considered. It is assumed that only biased approximations of the diffusion can be…
A Stable Particle Filter in High-Dimensions
Alex Beskos, Dan Crisan, Ajay Jasra +2
We consider the numerical approximation of the filtering problem in high dimensions, that is, when the hidden state lies in with large. For low dimensional probl…