By Finn V. Jensen, Thomas D. Nielsen (auth.)

Probabilistic graphical types and choice graphs are strong modeling instruments for reasoning and determination making less than uncertainty. As modeling languages they permit a typical specification of challenge domain names with inherent uncertainty, and from a computational point of view they aid effective algorithms for automated development and question answering. This contains trust updating, discovering the main possible cause of the saw facts, detecting conflicts within the facts entered into the community, selecting optimum concepts, studying for relevance, and appearing sensitivity analysis.

The booklet introduces probabilistic graphical types and determination graphs, together with Bayesian networks and impression diagrams. The reader is brought to the 2 forms of frameworks via examples and workouts, which additionally show the reader on the way to construct those versions.

The booklet is a brand new version of *Bayesian Networks and choice Graphs* by means of Finn V. Jensen. the hot variation is dependent into components. the 1st half makes a speciality of probabilistic graphical types. in comparison with the former booklet, the hot version additionally features a thorough description of modern extensions to the Bayesian community modeling language, advances in specific and approximate trust updating algorithms, and techniques for studying either the constitution and the parameters of a Bayesian community. the second one half bargains with selection graphs, and likewise to the frameworks defined within the past version, it additionally introduces Markov choice approaches and partly ordered selection difficulties. The authors additionally

- provide a well-founded functional advent to Bayesian networks, object-oriented Bayesian networks, choice bushes, impact diagrams (and editions hereof), and Markov choice processes.
- give sensible recommendation at the building of Bayesian networks, selection timber, and impression diagrams from area knowledge.
- give a number of examples and routines exploiting desktops for facing Bayesian networks and selection graphs.
- present an intensive advent to cutting-edge resolution and research algorithms.

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The booklet is meant as a textbook, however it is usually used for self-study and as a reference book.

Finn V. Jensen is a professor on the division of desktop technology at Aalborg collage, Denmark.

Thomas D. Nielsen is an affiliate professor on the comparable department.

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**Additional resources for Bayesian Networks and Decision Graphs: February 8, 2007**

**Sample text**

4. Let A be a variable with n states. A ﬁnding on A is an ndimensional table of zeros and ones. To distinguish between the statement e, “A is in either state i or j,” and the corresponding 0/1-ﬁnding vector, we sometimes use the boldface notation e for the ﬁnding. Semantically, a ﬁnding is a statement that certain states of A are impossible. Now, assume that you have a joint probability table, P (U), and let e be the preceding ﬁnding. The joint probability table P (U, e) is the table obtained from P (U) by replacing all entries with A not in state i or j by the value zero and leaving the other entries unchanged.

Assume that all variables in A’s Markov blanket are instantiated. Show that A is d-separated from the remaining uninstantiated variables. 9. 19. 10. Let D1 and D2 be DAGs over the same variables. The graph D1 is an I-submap of D2 if all d-separation properties of D1 also hold for D2 . If D2 is also an I-submap of D1 , they are said to be I-equivalent. 21 are I-equivalent? B A B C B C A B C A A C Fig. 21. 10. 11. Let {A1 , . . , An } be a topological ordering of the variables in a Bayesian network, and consider variable Ai with parents pa(Ai ).

S When S is an interval [a, b] (possibly inﬁnite), the outcomes are real numbers (such as height or weight), and you may be interested in the mean (height or weight). It is deﬁned as b μ= xf (x)dx, a and the variance is given by b σ2 = (μ − x)2 f (x)dx. 1. Given Axioms 1 to 3, prove that P (A ∪ B) = P (A) + P (B) − P (A ∩ B) . 2. Consider the experiment of rolling a red and a blue fair sixsided die. Give an example of a sample space for the experiment along with probabilities for each outcome. Suppose then that we are interested only in the sum of the dice (that is, the experiment consists in rolling the dice and adding up the numbers).