Master equations and the theory of stochastic path integrals
Markus F. Weber, Erwin Frey

TL;DR
This paper reviews master equations and their path integral representations, discussing analytical and numerical methods for solving them, especially under strong stochastic fluctuations, with applications to chemical reactions and rare event probabilities.
Contribution
It introduces novel mappings of master equations to PDEs and derives exact path integral representations, enhancing analytical tools for stochastic process analysis.
Findings
Path integral representations provide exact solutions to master equations.
Spectral, WKB, and variational methods are effective for PDE analysis.
Path integrals can approximate rare event probabilities.
Abstract
This review provides a pedagogic and self-contained introduction to master equations and to their representation by path integrals. We discuss analytical and numerical methods for the solution of master equations, keeping our focus on methods that are applicable even when stochastic fluctuations are strong. The reviewed methods include the generating function technique and the Poisson representation, as well as novel ways of mapping the forward and backward master equations onto linear partial differential equations (PDEs). Spectral methods, WKB approximations, and a variational approach have been proposed for the analysis of the PDE obeyed by the generating function. After outlining these methods, we solve the derived PDEs in terms of two path integrals. The path integrals provide distinct exact representations of the conditional probability distribution solving the master equations.…
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