Artificial Intelligence Frontiers in Statistics: AI and Statistics III.

• Author: Linster, Bruce G.

This volume contains a diverse selection of refereed papers that were presented at the Third International Workshop on Artificial Intelligence and Statistics, Fort Lauderdale, Florida, in January 1991. This collection of essays is very impressive in terms of the breadth of topics covered as well as the depth of analysis for each subject. Although this book is clearly not aimed at economists, students of the dismal science can benefit from this collection in two ways. First, some of the papers demonstrate how expert systems can help researchers do statistical analysis. Also, a number of essays show how statistical ideas can be applied within expert systems. Although seeing how research can be enhanced is important, the most interesting part of the book deals with the applications of statistical ideas.

The book begins with detailed descriptions of various expert systems. One paper, for instance, describes an expert system for experimental design, while others describe aids in developing linear models. The editor's paper in this section is particularly interesting. Professor Hand's proposal that metadata, which is information about data, be made explicit and available to the software so it can better guide the researcher is novel and potentially very important. The author applies his ideas to measurement scales as one form of metadata.

One particular area of Artificial Intelligence that has been significantly influenced by statistical ideas is belief networks. Part Two presents five very technical papers on the subject that will not be easily understandable by most economists. (I include myself in this group.) The papers in this section describe the relationship between belief networks and knowledge-based systems along with some specific examples.

Most readers of this journal have a profound interest in how learning takes place, and Part Three deals with the subject as it applies to artificial intelligence and learning algorithms. One paper, for example, describes a method for determining causation with background information as well as statistical data. There is also a paper that suggests a Bayesian tree learning algorithm, while another discusses a system for generating probabilistic networks. The discussions of learning algorithms in computing machines...