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Bayesian Networks and impression Diagrams: A consultant to building and research, moment Edition, presents a complete consultant for practitioners who desire to comprehend, build, and learn clever structures for determination aid according to probabilistic networks. This new version comprises six new sections, as well as fully-updated examples, tables, figures, and a revised appendix. meant essentially for practitioners, this booklet doesn't require refined mathematical talents or deep figuring out of the underlying thought and techniques nor does it speak about substitute applied sciences for reasoning less than uncertainty. the speculation and techniques provided are illustrated via greater than a hundred and forty examples, and routines are integrated for the reader to envision his or her point of realizing. The innovations and techniques provided for wisdom elicitation, version building and verification, modeling suggestions and tips, studying versions from facts, and analyses of versions have all been built and subtle at the foundation of various classes that the authors have held for practitioners around the globe.
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Extra info for Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
1 Basics This section introduces some axioms of probability theory and the fundamental concept of conditional probability, which provides the basis for probability calculus in discrete Bayesian networks. 1 Events The language of probability consists of statements (propositions) about probabilities of events. a/. , a coin flip), a particular observation of a value of a variable (or set of variables), an assignment of a value to a variable (or set of variables), etc. , a vector of values) of a subset of variables X Â V .
Deductive reasoning (sometimes referred to as causal reasoning) follows the direction of the causal links between variables of a model; for example, knowing that a patient suffers from angina, we can conclude (with high probability) the patient has fever and a sore throat. Abductive reasoning (sometimes referred to as diagnostic reasoning) goes against the direction of the causal links; for example, observing that a patient has a sore throat provides supporting evidence for angina being the correct diagnosis.
3: • Two serial connections B → A → W and E → A → W • One diverging connection A ← E → R • One converging connection B → A ← E In the following subsections, we discuss each of these three possible kinds of connections in terms of their ability to transmit information given evidence and given no evidence on the middle variable, and we shall see that it is the converging connection that provides the ability of probabilistic networks to perform intercausal reasoning (explaining away). 1 Serial Connections Let us consider the serial connection (causal chain) depicted in Fig.