6 citations · 7 across the 2 of their papers we have counts for
6 papers
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…
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…
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…