Asymptotic Expansions for Stationary Distributions of Perturbed Semi-Markov Processes

TL;DR

This paper introduces new algorithms for calculating asymptotic expansions of stationary distributions in perturbed semi-Markov processes, utilizing sequential phase space reduction techniques applicable to various process types.

Contribution

It presents novel algorithms based on phase space reduction for asymptotic analysis of stationary distributions in perturbed semi-Markov processes.

Findings

Algorithms effectively compute asymptotic expansions

Applicable to processes with coupled and uncoupled phase spaces

Enhances analytical tools for semi-Markov process analysis

Abstract

New algorithms for computing of asymptotic expansions for stationary distributions of nonlinearly perturbed semi-Markov processes are presented. The algorithms are based on special techniques of sequential phase space reduction, which can be applied to processes with asymptotically coupled and uncoupled finite phase spaces.