27 citations · 81 across the 21 of their papers we have counts for
25 papers
Diffusive Scaling Limits of Forward Event-Chain Monte Carlo: Provably Efficient Exploration with Partial Refreshment
Hirofumi Shiba, Kengo Kamatani
Piecewise deterministic Markov process samplers are attractive alternatives to Metropolis--Hastings algorithms. A central design question is how to incorporate partial velocity ref…
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…
Bayesian Inference for Non-Synchronously Observed Diffusions
Ajay Jasra, Kengo Kamatani, Amin Wu
We consider the problem of Bayesian inference for bi-variate data observed in time but with observation times which occur non-synchronously. In particular, this occurs in a wide va…
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 Monte Carlo for a class of Partially Observed Processes in Neuroscience
Mohamed Maama, Ajay Jasra, Kengo Kamatani
In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of mo…
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…
Scaling of Piecewise Deterministic Monte Carlo for Anisotropic Targets
Joris Bierkens, Kengo Kamatani, Gareth O. Roberts
Piecewise deterministic Markov processes (PDMPs) are a type of continuous-time Markov process that combine deterministic flows with jumps. Recently, PDMPs have garnered attention w…
Haar-Weave-Metropolis kernel
Kengo Kamatani, Xiaolin Song
Recently, many Markov chain Monte Carlo methods have been developed with deterministic reversible transform proposals inspired by the Hamiltonian Monte Carlo method. The determinis…
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…
High-dimensional scaling limits of piecewise deterministic sampling algorithms
Joris Bierkens, Kengo Kamatani, Gareth O. Roberts
Piecewise deterministic Markov processes are an important new tool in the design of Markov Chain Monte Carlo algorithms. Two examples of fundamental importance are the Bouncy Parti…
On One-Dimensional Riccati Diffusions
Adrian N. Bishop, Pierre Del Moral, Kengo Kamatani +1
This article is concerned with the fluctuation analysis and the stability properties of a class of one-dimensional Riccati diffusions. These one-dimensional stochastic differential…
Bayesian inference for Stable Levy driven Stochastic Differential Equations with high-frequency data
Ajay Jasra, Kengo Kamatani, Hiroki Masuda
In this article we consider parametric Bayesian inference for stochastic differential equations (SDE) driven by a pure-jump stable Levy process, which is observed at high frequency…
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…
Ergodicity of Markov chain Monte Carlo with reversible proposal
Kengo Kamatani
We describe ergodic properties of some Metropolis-Hastings (MH) algorithms for heavy-tailed target distributions. The analysis usually falls into sub-geometric ergodicity framework…
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…
Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution
Kengo Kamatani
The purpose of this paper is to introduce a new Markov chain Monte Carlo method and exhibit its efficiency by simulation and high-dimensional asymptotic theory. Key fact is that ou…
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…
Rate optimality of Random walk Metropolis algorithm in high-dimension with heavy-tailed target distribution
Kengo Kamatani
The choice of the increment distribution is crucial for the random-walk Metropolis-Hastings (RWM) algorithm. In this paper we study the optimal choice in high-dimension setting amo…
Local degeneracy of Markov chain Monte Carlo methods
Kengo Kamatani
We study asymptotic behavior of Monte Carlo method. Local consistency is one of an ideal property of Monte Carlo method. However, it may fail to hold local consistency for several…
Weak consistency of Markov chain Monte Carlo methods
Kengo Kamatani
Markov chain Monte Calro methods (MCMC) are commonly used in Bayesian statistics. In the last twenty years, many results have been established for the calculation of the exact conv…