Malliavin Weight Sampling: A Practical Guide

Patrick B. Warren*, Rosalind J. Allen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Malliavin weight sampling (MWS) is a stochastic calculus technique for computing the derivatives of averaged system properties with respect to parameters in stochastic simulations, without perturbing the system's dynamics. It applies to systems in or out of equilibrium, in steady state or time-dependent situations, and has applications in the calculation of response coefficients, parameter sensitivities and Jacobian matrices for gradient-based parameter optimisation algorithms. The implementation of MWS has been described in the specific contexts of kinetic Monte Carlo and Brownian dynamics simulation algorithms. Here, we present a general theoretical framework for deriving the appropriate MWS update rule for any stochastic simulation algorithm. We also provide pedagogical information on its practical implementation.

Original languageEnglish
Pages (from-to)221-232
Number of pages12
JournalEntropy
Volume16
Issue number1
DOIs
Publication statusPublished - Jan 2014

Keywords

  • stochastic calculus
  • Brownian dynamics

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