Encyclopedia of Operations Research and Management Science
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#1997 Operational Research Society Ltd. All rights reserved. 0160-5682/97 $12.00
Book selection Edited by JM Wilson A Gammerman: Computational Learning and Probabilistic Reasoning C Huxham: Creating Collaborative Advantage B Kolman and RE Beck: Elementary Linear Programming and MJ Panik: Linear Programming SI Gass and CM Harris: Encyclopedia of Operations Research and Management Science M Wisniewski and R Stead: Foundation Quantitative Methods for Business JM Collins and TW Ruel®: Strategic Risk: A State De®ned Approach L Bianco and P Toth (Eds): Advanced Methods in Transportation Analysis R. Saigal: Linear Programming: A Modern Integrated Analysis M Alvesson and H Willmott: Making Sense of Management: A Critical Introduction M Daskin: Network and Discrete Location: Models, Algorithms and Applications P Chretienne, EG Coffman, JK Lenstra and Z Liu (Eds): Scheduling Theory and its Applications
Computational Learning and Probabilistic Reasoning A Gammerman (ed) John Wiley & Sons, Chichester, in association with UNICOM, 1996 xxv 312 pp. £40.00 ISBN 0471 96279 This book has arisen out of a conference on Applied Decision Technologies (ADT95) run by UNICOM Seminars and held in London in April 1995. There are eighteen research papers organised into four sections. This is not a book for the novice in this ®eld but should provide an invaluable resource to the researcher. The editor has ensured that the material is largely well presented, with a uniform style and a good index. As is so often the case with such collections of research material, the lack of an introductory chapter and of a coherent theme throughout the book make it unapproachable as a single volume. The division into four sections is an attempt to make it more readable but it is sometimes dif®cult to follow the criteria used for such a partition. The ®rst section of the book describes several inductive principles and techniques used in computational learning. Vladimar Vapnik develops the structure of a statistical learning theory, discussing the problem of learning from examples using statistical techniques. Subsequent papers in this section cover stochastic complexity, MML inference of predictive trees, graphs and nets, the association between information compression and reasoning and two denotational learning models. Section two of the book contains material on causal probabilistic models. The chapter by J Pearl provides a very readable summary of the recent advances in causal reason-
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ing and causal graphs. In the subsequent chapter, VG Vovk develops some of Pearl's own ideas and proposes a new semantics for Pearl's action calculus. Although this provides quite demanding reading, this chapter contains some stimulating ideas. The remaining two chapters of the section have a more practical focus, one on ef®cient estimation and model selection in large graphical models and the other on the use of graphical models to solve some problems in multivariate statistical analysis. It is Section Three though that provides
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