By Finn V. Jensen, Thomas D. Nielsen (auth.)
ISBN-10: 0387682813
ISBN-13: 9780387682815
ISBN-10: 0387682821
ISBN-13: 9780387682822
Probabilistic graphical versions and determination graphs are strong modeling instruments for reasoning and determination making less than uncertainty. As modeling languages they permit a traditional specification of challenge domain names with inherent uncertainty, and from a computational viewpoint they help effective algorithms for computerized development and question answering. This contains trust updating, discovering the main possible reason behind the saw proof, detecting conflicts within the proof entered into the community, deciding upon optimum techniques, examining for relevance, and appearing sensitivity analysis.
The ebook introduces probabilistic graphical types and selection graphs, together with Bayesian networks and effect diagrams. The reader is brought to the 2 kinds of frameworks via examples and routines, which additionally coach the reader on find out how to construct those types.
The publication is a brand new variation of Bayesian Networks and choice Graphs through Finn V. Jensen. the hot version is established into elements. the 1st half makes a speciality of probabilistic graphical types. in comparison with the former e-book, the recent version additionally contains a thorough description of modern extensions to the Bayesian community modeling language, advances in particular and approximate trust updating algorithms, and strategies for studying either the constitution and the parameters of a Bayesian community. the second one half bargains with selection graphs, and also to the frameworks defined within the prior version, it additionally introduces Markov choice techniques and partly ordered selection difficulties. The authors additionally
- provide a well-founded useful creation to Bayesian networks, object-oriented Bayesian networks, choice bushes, impact diagrams (and editions hereof), and Markov choice processes.
- give sensible suggestion at the building of Bayesian networks, choice bushes, and impression diagrams from area knowledge.
- give numerous examples and routines exploiting computers for facing Bayesian networks and selection graphs.
- present a radical creation to state of the art answer and research algorithms.
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The e-book 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 technological know-how at Aalborg college, Denmark.
Thomas D. Nielsen is an affiliate professor on the related department.
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Additional resources for Bayesian Networks and Decision Graphs: February 8, 2007
Example text
0, 1, 0, . . , 0), where the 1’s are at the i’th and j’th places. 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.
14 1 Prerequisites on Probability Theory For marginalization of a product, the following holds 6. The distributive law: If A ∈ / dom(φ1 ), then A φ1 φ2 = φ1 A φ2 . The distributive law is usually known as ab + ac = a(b + c), and the preceding formula is actually the same law applied to tables. 14. 14 are equal and correspond to the left-hand and right-hand sides of the distributive law. 10. φ1 (A, B) and φ2 (C, B). 11. φ1 (A, B) · φ2 (C, B). The two numbers in each entry correspond to the states c1 and c2 .
1). However, P (U) grows exponentially with the number of variables, and U need not be very large before the table becomes intractably large. , a way of storing information from which P (U) can be calculated if needed. Let BN be a Bayesian network over U, and let P (U) be a probability distribution reflecting the properties specified by BN : (i) the conditional probabilities for a variable given its parents in P (U) must be as specified in BN , and (ii) if the variables A and B are d-separated in BN given the set C, then A and B are independent given C in P (U).
Bayesian Networks and Decision Graphs: February 8, 2007 by Finn V. Jensen, Thomas D. Nielsen (auth.)
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