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Bayesian Networks and Decision Graphs by Thomas Dyhre Nielsen, FINN VERNER JENSEN

By Thomas Dyhre Nielsen, FINN VERNER JENSEN

Probabilistic graphical types and choice graphs are robust modeling instruments for reasoning and choice making below uncertainty. As modeling languages they permit a typical specification of challenge domain names with inherent uncertainty, and from a computational viewpoint they aid effective algorithms for automated development and question answering. This comprises trust updating, discovering the main possible cause of the saw proof, detecting conflicts within the facts entered into the community, choosing optimum concepts, reading for relevance, and acting sensitivity analysis.

The e-book introduces probabilistic graphical types and determination graphs, together with Bayesian networks and impact diagrams. The reader is brought to the 2 kinds of frameworks via examples and routines, which additionally teach the reader on the best way to construct those versions.

The e-book is a brand new version of Bayesian Networks and selection Graphs through Finn V. Jensen. the recent variation is based into components. the 1st half makes a speciality of probabilistic graphical types. in comparison with the former ebook, the hot version additionally incorporates a thorough description of modern extensions to the Bayesian community modeling language, advances in special 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 choice graphs, and also to the frameworks defined within the prior variation, it additionally introduces Markov determination approaches and in part ordered choice difficulties. The authors additionally

    • provide a well-founded functional creation to Bayesian networks, object-oriented Bayesian networks, selection bushes, effect diagrams (and editions hereof), and Markov determination processes.
    • give functional recommendation at the building of Bayesian networks, determination timber, and effect diagrams from area knowledge.
    • give numerous examples and routines exploiting desktops for facing Bayesian networks and determination graphs.
    • present a radical advent to state of the art resolution and research algorithms.

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

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Sample text

2. 3 Building Models The framework of Bayesian networks is a very efficient language for building models of domains with inherent uncertainty. 6, it is a tedious job to perform evidence transmission even for very simple Bayesian networks. Fortunately, software tools that can do the calculational job for us are available. In the rest of this book, we assume that the reader has access to such a system (some URLs are given in the preface). Therefore, we can start by concentrating on how to use Bayesian networks in model building and defer a presentation of methods for probability updating to Chapter 4.

An−1 )P (A1 , . . , An−1 ), P (A1 , . . , An−1 ) = P (An−1 | A1 , . . , An−2 )P (A1 , . . , An−2 ), .. P (A1 , A2 ) = P (A2 | A1 )P (A1 ). 1 (The chain rule for Bayesian networks). Let BN be a Bayesian network over U = {A1 , . . , An }. 3 Bayesian Networks 37 where pa(Ai ) are the parents of Ai in BN , and P (U) reflects the properties of BN . Proof. First we should show that P (U) is indeed a probability distribution. That is, we need to show that Axioms 1–3 hold. 15). Next we prove that the specification of BN is consistent, so that P (U) reflects the properties of BN .

The Markov blanket of a variable A is the set consisting of the parents of A, the children of A, and the variables sharing a child with A. 12). You may wonder why we have introduced d-separation as a definition rather than as a theorem. A theorem should be as follows. Claim: If A and B are d-separated, then changes in the certainty of A have no impact on the certainty of B. ” You can take d-separation as a property of human reasoning and require that any certainty calculus should comply with the claim.

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