Optimal Fluctuations for Discrete-time Markov Jump Processes

TL;DR

This paper demonstrates that the focusing effect of large fluctuations onto optimal paths, known from Langevin dynamics, also exists in discrete-time Markov jump processes, using large deviation theory and time reversal concepts.

Contribution

It extends the concept of fluctuation focusing to discrete-time Markov jump processes and establishes a relationship between optimal paths and time-reversed distributions.

Findings

Focusing effect persists in Markov jump processes.

Optimal paths relate to time-reversed probability distributions.

Provides a theoretical framework for deterministic emergence from stochastic events.

Abstract

In the last few decades, noise-induced large fluctuations and transition phenomena have garnered significant attention in a variety of scientific contexts. The concept of prehistory probability has been proposed within the framework of Langevin dynamics to illustrate the focusing effect of large fluctuation paths onto a deterministic trajectory known as the optimal path. The present paper is devoted to showing that such a focusing effect persists within the framework of discrete-time Markov jump processes. Our proof leverages large deviation theory and the concept of time reversal for Markov jump processes. A key finding is the relationship identified between the optimal path and the time reversal of a specific family of probability distributions. This theoretical framework elucidates how an essentially deterministic mechanism can emerge from rare stochastic events in discrete-time Markov jump systems.