{"title":"Compact representations of probability distributions in the analysis of Superposed GSPNs","authors":"P. Buchholz, P. Kemper","doi":"10.1109/PNPM.2001.953358","DOIUrl":null,"url":null,"abstract":"Markov chain based analysis of GSPNs suffers from the state space explosion problem. In this paper we combine ideas from two different other approaches to analyze systems with very large state spaces. First, we represent the generator matrix as a sum of Kronecker products of small component matrices. Second, we use an extension of probabilistic decision graphs to represent probability vectors. The combination of these two concepts is the base for an iterative solution technique with the potential to handle extremely large Markov chains resulting from Superposed GSPNs or related model types.","PeriodicalId":364695,"journal":{"name":"Proceedings 9th International Workshop on Petri Nets and Performance Models","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2001-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 9th International Workshop on Petri Nets and Performance Models","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PNPM.2001.953358","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
Markov chain based analysis of GSPNs suffers from the state space explosion problem. In this paper we combine ideas from two different other approaches to analyze systems with very large state spaces. First, we represent the generator matrix as a sum of Kronecker products of small component matrices. Second, we use an extension of probabilistic decision graphs to represent probability vectors. The combination of these two concepts is the base for an iterative solution technique with the potential to handle extremely large Markov chains resulting from Superposed GSPNs or related model types.