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In this chapter, we continue the presentation of probabilistic modeling. ► Section 23.2 introduces the Bayesian Belief Network, BBN, a directed acyclic graph that can offer significant computational advantages over full Bayesian reasoning. In ► Sect. 23.3, we present Dynamic Bayesian Networks, a graphical modeling technique that tracks probabilistic information across time and situation changes. ► Section 23.4 introduces observable Markov models with several examples. Hidden Markov models, with several extensions, are presented in ► Chap. 24 .

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Bayesian Belief Networks and Observable Markov Models

  • 章节

  • 发表日期：

    2025年

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  • George F. Luger

摘要

In this chapter, we continue the presentation of probabilistic modeling. ► Section 23.2 introduces the Bayesian Belief Network, BBN, a directed acyclic graph that can offer significant computational advantages over full Bayesian reasoning. In ► Sect. 23.3, we present Dynamic Bayesian Networks, a graphical modeling technique that tracks probabilistic information across time and situation changes. ► Section 23.4 introduces observable Markov models with several examples. Hidden Markov models, with several extensions, are presented in ► Chap. 24 .