This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.Assuming that only one problem exists, the probabilities of the three problems are 0.8, 0.15, and 0.05, respectively. ... The probability of action 4 solving the problem is 1 no matter what the problem is and which other attempts to solve the problem have failed so far. Action 3 ... Continue Example 10.3 and perform one more iteration of value iteration starting with the utility function shown in Figure 10.29(c).

Title | : | Bayesian Networks and Decision Graphs |

Author | : | Thomas Dyhre Nielsen, FINN VERNER JENSEN |

Publisher | : | Springer Science & Business Media - 2007-06-06 |

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