Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Kamatani, Kengo"'
Autor:
Andral, Charly, Kamatani, Kengo
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 algorithms. However, the implementation of the processes is challenging due to
Externí odkaz:
http://arxiv.org/abs/2408.03682
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 models can be routinely found in applications, of which we focus upon th
Externí odkaz:
http://arxiv.org/abs/2310.06533
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 equation (ODE) drives the deterministic element and can only be solved n
Externí odkaz:
http://arxiv.org/abs/2309.02998
Piecewise deterministic Markov processes (PDMPs) are a type of continuous-time Markov process that combine deterministic flows with jumps. Recently, PDMPs have garnered attention within the Monte Carlo community as a potential alternative to traditio
Externí odkaz:
http://arxiv.org/abs/2305.00694
Autor:
Kamatani, Kengo, Song, Xiaolin
Recently, many Markov chain Monte Carlo methods have been developed with deterministic reversible transform proposals inspired by the Hamiltonian Monte Carlo method. The deterministic transform is relatively easy to reconcile with the local informati
Externí odkaz:
http://arxiv.org/abs/2111.06148
Autor:
Beskos, Alexandros, Kamatani, Kengo
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 carry out a major step in covering this gap by developing the proper the
Externí odkaz:
http://arxiv.org/abs/2008.02906
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 target density as a density, $e^{-U}$, with respect to a prescribed (usuall
Externí odkaz:
http://arxiv.org/abs/2006.13777
Autor:
Kamatani, Kengo, Song, Xiaolin
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 introduce a projection that maps a state space to a totally ordered group. B
Externí odkaz:
http://arxiv.org/abs/2005.05584
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 Particle Sampler (BPS) and the Zig-Zag process (ZZ). In this paper scaling
Externí odkaz:
http://arxiv.org/abs/1807.11358
Publikováno v:
The Annals of Applied Probability, Volume 29, No. 2, pages: 1127-1187, 2019
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 equations exhibit a quadratic drift function and a non-Lipschitz cont
Externí odkaz:
http://arxiv.org/abs/1711.10065