In the last line of page 110, read “less” for “more.” The proof on page 115 is nonsensical, as it gives a probability of less than 1 as a product of terms greater than or equal to 1, although the theorem itself may be true.Ĭhapters 4 through 8 give the essence of Pearl's probabilistic techniques under the titles “Belief Updating,” “Distributed Revision of Composite Beliefs,” “Decision and Control,” “Continuous Variables and Uncertain Probabilities,” and “Learning Structure from Data.” The author also discusses capturing the essence of induction, but science proceeds by induction only in the imagination of philosophers. This deficiency is especially unfortunate because Nilsson's probabilistic logic, which is discussed briefly in chapter 9, can give a sound basis to all computations: it is universal, its consistency can be enforced by a simple set of inequalities, and it gives a framework for applying Dempster's original theory for multiple inferences.Ĭhapter 3 deals with the graph-theoretical background for Markov and Bayesian networks and the use of such networks for computing conditional probabilities. Also, Pearl's theory would be more plausible if he had introduced sample spaces. His terminology is not always clear-conditional probabilities are not really probabilities rather, P(X &vbm0 Y) is the weight of P(Y) in the computation of P(X) by partition. By disregarding absolute probabilities, he also omits some consequences of additional information. Unfortunately, he uses probabilities in de Finetti's statistical sense instead of treating probability as an abstract basis of statistics. In the first two chapters Pearl establishes his approach he concentrates on using Bayes's formula to compute conditional probabilities for decision-making (rather than Bayes's rule, which implies using uniform probabilities in case of ignorance). The book is divided into three parts: chapters 1–3, 4–8, and 9 and 10. A few misprints and questionable statements appear, but the author states his views and techniques clearly, and everybody trying to choose a conceptual framework for her or his AI programs should study this book. The bibliography is ample but obviously biased toward the author's views, and the index seems adequate. A researcher active in a field usually cannot write a balanced textbook Pearl develops competing approaches only far enough to demolish their bases. Although Pearl says the book is based on his lecture notes, it is valuable as a report on current research and not as a balanced introduction to reasoning systems. It is easier to absorb the background of a researcher's work from a coherent exposition than from many disjointed papers, so this book by Judea Pearl, a leading researcher in AI reasoning systems, makes a valuable contribution to this active field.
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