Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Bierkens, Joris"'
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
We extend Monte Carlo samplers based on piecewise deterministic Markov processes (PDMP samplers) by formally defining different boundary conditions such as sticky floors, soft and hard walls and teleportation portals. This allows PDMP samplers to tar
Externí odkaz:
http://arxiv.org/abs/2303.08023
Autor:
Bierkens, Joris, Duncan, Andrew
When performing Bayesian computations in practice, one is often faced with the challenge that the constituent model components and/or the data are only available in a distributed fashion, e.g. due to privacy concerns or sheer volume. While various me
Externí odkaz:
http://arxiv.org/abs/2210.13816
Autor:
Dobson, Paul, Bierkens, Joris
In this paper we aim to construct infinite dimensional versions of well established Piecewise Deterministic Monte Carlo methods, such as the Bouncy Particle Sampler, the Zig-Zag Sampler and the Boomerang Sampler. In order to do so we provide an abstr
Externí odkaz:
http://arxiv.org/abs/2205.11452
Autor:
Pengel, Ardjen, Bierkens, Joris
Strong invariance principles describe the error term of a Brownian approximation of the partial sums of a stochastic process. While these strong approximation results have many applications, the results for continuous-time settings have been limited.
Externí odkaz:
http://arxiv.org/abs/2111.12603
Piecewise deterministic Markov processes (PDMPs) are a class of stochastic processes with applications in several fields of applied mathematics spanning from mathematical modeling of physical phenomena to computational methods. A PDMP is specified by
Externí odkaz:
http://arxiv.org/abs/2109.11827
Publikováno v:
Stat Comput 33, 8 (2023)
We construct a new class of efficient Monte Carlo methods based on continuous-time piecewise deterministic Markov processes (PDMPs) suitable for inference in high dimensional sparse models, i.e. models for which there is prior knowledge that many coo
Externí odkaz:
http://arxiv.org/abs/2103.08478
Autor:
Bertazzi, Andrea, Bierkens, Joris
The Bouncy Particle sampler (BPS) and the Zig-Zag sampler (ZZS) are continuous time, non-reversible Monte Carlo methods based on piecewise deterministic Markov processes. Experiments show that the speed of convergence of these samplers can be affecte
Externí odkaz:
http://arxiv.org/abs/2012.13924
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
We introduce the use of the Zig-Zag sampler to the problem of sampling conditional diffusion processes (diffusion bridges). The Zig-Zag sampler is a rejection-free sampling scheme based on a non-reversible continuous piecewise deterministic Markov pr
Externí odkaz:
http://arxiv.org/abs/2001.05889