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作者机构:Sorbonne Univ Natl Inst Math Sci INSMI Probabil & Stat LPSM CNRS 8001LPMACNRS 7599 F-75005 Paris France
出 版 物:《METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY》 (应用概率论的方法与计算)
年 卷 期:2019年第21卷第3期
页 面:875-887页
核心收录:
学科分类:07[理学] 070104[理学-应用数学] 0714[理学-统计学(可授理学、经济学学位)] 0701[理学-数学]
主 题:Probability generating function Moment generating function Probabilistic graphical model Bayesian network Sum-product algorithm Forward backward algorithms
摘 要:We focus here on the distribution of the random count N of a regular expression in a multi-state random sequence generated by a heterogenous Markov source. We first briefly recall how classical Markov chain embedding techniques allow reducing the problem to the count of specific transitions in a (heterogenous) order 1 Markov chain over a deterministic finite automaton state space. From this result we derive the expression of both the mgf/pgf of N as well as the factorial and non-factorial moments of N. We then introduce the notion of evidence-based constraints in this context. Following the classical forward/backward algorithm in hidden Markov models, we provide explicit recursions allowing to compute the mgf/pgf of N under the evidence constraint. All the results presented are illustrated with a toy example.