{"title":"Efficient sampling from phase-type distributions","authors":"","doi":"10.1016/j.orl.2024.107184","DOIUrl":null,"url":null,"abstract":"<div><p>A phase-type distributed random variable represents the time to absorption of a Markov chain with an absorbing state. In this letter, we show that the alias method can be modified to efficiently generate phase-type distributed random variables. Both initialisation and generation are fast, at the cost of larger memory requirements. Numerical experiments show that the proposed method significantly reduces the computation time compared to direct simulation.</p></div>","PeriodicalId":54682,"journal":{"name":"Operations Research Letters","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research Letters","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167637724001202","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
A phase-type distributed random variable represents the time to absorption of a Markov chain with an absorbing state. In this letter, we show that the alias method can be modified to efficiently generate phase-type distributed random variables. Both initialisation and generation are fast, at the cost of larger memory requirements. Numerical experiments show that the proposed method significantly reduces the computation time compared to direct simulation.
期刊介绍:
Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.