By Kevin B. Korb

Updated and elevated, Bayesian synthetic Intelligence, moment Edition offers a pragmatic and available creation to the most ideas, beginning, and functions of Bayesian networks. It makes a speciality of either the causal discovery of networks and Bayesian inference techniques. Adopting a causal interpretation of Bayesian networks, the authors speak about using Bayesian networks for causal modeling. in addition they draw all alone utilized examine to demonstrate numerous purposes of the technology.

New to the second one Edition

    • New bankruptcy on Bayesian community classifiers
    • New part on object-oriented Bayesian networks
    • New part that addresses foundational issues of causal discovery and Markov blanket discovery
    • New part that covers equipment of comparing causal discovery programs
    • Discussions of many universal modeling errors
    • New functions and case studies
    • More insurance at the makes use of of causal interventions to appreciate and cause with causal Bayesian networks

    Illustrated with actual case experiences, the second one variation of this bestseller maintains to hide the basis of Bayesian networks. It offers the weather of Bayesian community know-how, computerized causal discovery, and studying possibilities from info and exhibits the right way to hire those applied sciences to enhance probabilistic professional systems.

    Web Resource
    The book’s site at www.csse.monash.edu.au/bai/book/book.html deals a number of supplemental fabrics, together with instance Bayesian networks and information units. teachers can electronic mail the authors for pattern recommendations to the various difficulties within the text.

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    Additional resources for Bayesian Artificial Intelligence, Second Edition

    Example text

    A mother was arrested after her second baby died a few months old, apparently of sudden infant death syndrome (SIDS), exactly as her first child had died a year earlier. According to prosecution testimony, about 2 in 17200 babies die of SIDS. So, according to their argument, there is only a probability of (2/17200)2 ≈ 1/72000000 that two such deaths would happen in the same family by chance alone. In other words, according to the prosecution, the woman was guilty beyond a reasonable doubt. The jury returned a guilty verdict, even though there was no significant evidence of guilt presented beyond this argument.

    By looking at the mass of the density function over intervals. 4 Bayesian Artificial Intelligence, Second Edition Interpretations of probability There have been two main contending views about how to understand probability. One asserts that probabilities are fundamentally dispositional properties of non-deterministic physical systems, the classical such systems being gambling devices, such as dice. This view is particularly associated with frequentism, advocated in the 19th century by John Venn (1866), identifying probabilities with long-run frequencies of events.

    2 9 Variables Although we have introduced probabilities over events, in most of our discussion we shall be concerned with probabilities over random variables. A random variable is a variable which reports the outcome of some measurement process. It can be related to events, to be sure. For example, instead of talking about which event in a partition {Ai } turns out to be the case, we can equivalently talk about which state xi the random variable X takes, which we write X = xi . The set of states a variable X can take form its state space, written ΩX , and its size (or arity) is |ΩX |.

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