文章基本信息

标题：Tau-leaped Particle Learning
本地全文：下载
作者：Jarad Niemi ; Michael Ludkovski
期刊名称：Online Journal of Public Health Informatics
电子版ISSN：1947-2579
出版年度：2013
卷号：5
期号：1
语种：English
出版社：University of Illinois at Chicago
摘要：Development of effective policy interventions to stem disease outbreaks requires knowledge of the current state of affairs, e.g. how many individuals are currently infected, a strain's virulence, etc, as well as our uncertainty of these values. A Bayesian inferential approach provides this information, but at a computational expense. We develop a sequential Bayesian approach based on an epidemiological compartment model and noisy count observations of the transitions between compartments.