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Advanced MCMC methods for sampling on diffusion pathspace

Beskos, Alexandros, Kalogeropoulos, Konstantinos and Pazos, Erik (2013) Advanced MCMC methods for sampling on diffusion pathspace. Stochastic Processes and Their Applications, 123 (4). pp. 1415-1453. ISSN 0304-4149

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Identification Number: 10.1016/


The need to calibrate increasingly complex statistical models requires a persistent effort for further advances on available, computationally intensive Monte-Carlo methods. We study here an advanced version of familiar Markov-chain Monte-Carlo (MCMC) algorithms that sample from target distributions defined as change of measures from Gaussian laws on general Hilbert spaces. Such a model structure arises in several contexts: we focus here at the important class of statistical models driven by diffusion paths whence the Wiener process constitutes the reference Gaussian law. Particular emphasis is given on advanced Hybrid Monte-Carlo (HMC) which makes large, derivative driven steps in the state space (in contrast with local-move Random-walk-type algorithms) with analytical and experimental results. We illustrate it’s computational advantages in various diffusion processes and observation regimes; examples include stochastic volatility and latent survival models. In contrast with their standard MCMC counterparts, the advanced versions have mesh-free mixing times, as these will not deteriorate upon refinement of the approximation of the inherently infinite-dimensional diffusion paths by finite-dimensional ones used in practice when applying the algorithms on a computer.

Item Type: Article
Official URL:
Additional Information: © 2012 Elsevier B.V.
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 05 Dec 2012 09:17
Last Modified: 20 Feb 2019 10:28

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