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Article

A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space

Department of Physics, University of Cincinnati, Cincinnati, OH 45221, USA
Academic Editors: Michael Dellnitz, Carsten Hartmann and Feliks Nüske
Entropy 2021, 23(5), 499; https://doi.org/10.3390/e23050499
Received: 23 March 2021 / Revised: 14 April 2021 / Accepted: 15 April 2021 / Published: 22 April 2021
To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, hybrid Monte Carlo on Hilbert spaces (Beskos, et al. Stoch. Proc. Applic. 2011), that provides finite-dimensional approximations of measures π, which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the algorithm described in this article lies in the choice of this operator. This new choice defines a Markov Chain Monte Carlo (MCMC) method that is well defined on the Hilbert space itself. As before, the algorithm described herein uses an enlarged phase space Π having the target π as a marginal, together with a Hamiltonian flow that preserves Π. In the previous work, the authors explored a method where the phase space π was augmented with Brownian bridges. With this new choice, π is augmented by Ornstein–Uhlenbeck (OU) bridges. The covariance of Brownian bridges grows with its length, which has negative effects on the acceptance rate in the MCMC method. This contrasts with the covariance of OU bridges, which is independent of the path length. The ingredients of the new algorithm include the definition of the mass operator, the equations for the Hamiltonian flow, the (approximate) numerical integration of the evolution equations, and finally, the Metropolis–Hastings acceptance rule. Taken together, these constitute a robust method for sampling the target distribution in an almost dimension-free manner. The behavior of this novel algorithm is demonstrated by computer experiments for a particle moving in two dimensions, between two free-energy basins separated by an entropic barrier. View Full-Text
Keywords: Brownian dynamics; stochastic processes; sampling path space; transition paths Brownian dynamics; stochastic processes; sampling path space; transition paths
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MDPI and ACS Style

Pinski, F.J. A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space. Entropy 2021, 23, 499. https://doi.org/10.3390/e23050499

AMA Style

Pinski FJ. A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space. Entropy. 2021; 23(5):499. https://doi.org/10.3390/e23050499

Chicago/Turabian Style

Pinski, Francis J. 2021. "A Novel Hybrid Monte Carlo Algorithm for Sampling Path Space" Entropy 23, no. 5: 499. https://doi.org/10.3390/e23050499

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