By Uffe B. Kjærulff, Anders L. Madsen
Bayesian Networks and effect Diagrams: A consultant to building and research, moment Edition, offers a accomplished advisor for practitioners who desire to comprehend, build, and research clever structures for selection help in line with probabilistic networks. This re-creation comprises six new sections, as well as fully-updated examples, tables, figures, and a revised appendix. meant basically for practitioners, this publication doesn't require subtle mathematical abilities or deep figuring out of the underlying idea and strategies nor does it talk about substitute applied sciences for reasoning below uncertainty. the idea and strategies offered are illustrated via greater than one hundred forty examples, and routines are integrated for the reader to examine his or her point of figuring out. The suggestions and techniques awarded for wisdom elicitation, version development and verification, modeling thoughts and methods, studying versions from facts, and analyses of versions have all been built and sophisticated at the foundation of diverse classes that the authors have held for practitioners around the globe.
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Additional info for Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Then, to answer the query, we consult this graph to see if there is a path from a vertex in A to a vertex in B that do not contain a vertex in S . G B | S . , the graphical structure) of probabilistic networks that are given by DAGs. We defined the notion of the moral graph of a DAG, which plays an important role in understanding the independence properties represented by a DAG and in generating a junction tree for making inference in a probabilistic network (cf. Chap. 5). We introduced the taxonomy of variables and vertices (the nodes of the DAG of a probabilistic network that represent the chance and decision variables and the utility functions of the network) and discussed the notions of product spaces over the domains of variables and projections down to smaller-dimensional spaces, which play a crucial role in inference processes (in Chap.
For example, if v is a vertex representing a variable, then we denote that variable by Xv . v/ denotes the domain of the function, which is a set of chance and/or decision variables. 3 Taxonomy of Vertices/Variables For convenience, we shall use the following terminology for classifying variables and/or vertices of probabilistic networks. First, as discussed above, there are three main classes of vertices in probabilistic networks, namely, vertices representing chance variables, vertices representing decision variables, and vertices representing utility functions.
X2 D red | X1 D red/ 2 1 10 9 1 , D 45 D etc. X1 D blue; X2 / 0 1 0 1 0 1 0 1 1 1 1 1 B 45 C B 15 C B 9 C B 5 C B C B C B C B C B 1 C B 1 C B1C B 3 C B B B B C C C C. DB CCB CCB CDB C B 15 C B 15 C B 6 C B 10 C @ 1 A @ 1 A @2A @ 1 A 9 6 9 2 That is, the probabilities of getting a red, a green, and a blue ball in the second draw are, respectively, 0:2, 0:3, and 0:5, given that we know nothing about the color of the first ball. 0:1111; 0:3333; 0:5556/; that is, once the color of the first ball is known, our belief about the color of the second changes.