AUTOMATIC LEARNING FOR DYNAMIC MARKOV-FIELDS WITH APPLICATION TO EPIDEMIOLOGY
成果类型：
Article
署名作者：
YAKOWITZ, S; HAYES, R; GANI, J
署名单位：
Sun Microsystems, Inc.; Sun Microsystems, Inc.; University of California System; University of California Santa Barbara
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.40.5.867
发表日期：
1992
页码：
867-876
关键词：
摘要：
Following an outline of dynamic Markov fields, we briefly describe some spatial models for contagious diseases and pose a prototype epidemic control problem. The notion of automatic learning is then introduced, and its relevance to epidemic control is described. In essence, once a contagion model is adopted and a domain of controls has been selected, learning can be used to obtain asymptotically optimal performance. (The learning algorithm is a synthesis of simulation and optimization, and is a suitable alternative to response surface methodology, in many applications.) The end product is the same optimal control as would be obtained by a conventional analysis. The point is that our current understanding of dynamic Markov fields does not permit conventional analysis; automatic learning has no computationally competitive alternative. The theory is illustrated by application to a spatial epidemic control problem.