Analysis of Petri Net Models through Stochastic Differential Equations

摘要: It is well known, mainly because of the work Kurtz, that density dependent Markov chains can be approximated by sets ordinary differential equations (ODEs) when their indexing parameter grows very large. This approximation cannot capture stochastic nature process and, consequently, it provide an erroneous view behavior chain if not sufficiently high. Important phenomena revealed include non-negligible variance and bi-modal population distributions. A less-known proposed Kurtz applies (SDEs) provides information about process.