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This book will be of interest to statisticians of AI researchers who want to apply AI techniques in statistics. It will also be of interest to AI researches or statisticians studying propagation of uncertainty or learning (clustering concept formation).

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CONTENTS
1. Overview / William A. Gale = 1
 1.1 Opportunities = 1
 1.2 Background = 4
 1.3 Introduction to Chapters = 10
2. A Statistical View of Uncertainty in Expert Systems / David J. Spiegelhalter = 17
 2.1 Introduction = 17
 2.2 The Aims of Expert Systems = 18
 2.3 Uncertainty in Expert Systems = 20
 2.4 Probability : Is It Appropriate, Necessary, or Practical? = 31
 2.5 Expert Systems and Subjectivist Bayesian Statistics = 33
 2.6 Aspects of Probabilistic Reasoning = 39
 2.7 Conclusions = 48
3. Knowledge, Decision Making, and Uncertainty / John Fox = 57
 3.1 Introduction = 57
 3.2 A Brief Review of Uncertainty in Expert Systems = 59
 3.3 A Generalized View of Belief = 61
 3.4 An Analysis of Knowledge and Belief = 62
 3.5 Applications = 66
 3.6 Precision and the Attachment of Numerical Procedures = 67
 3.7 Combination of Beliefs = 69
 3.8 Validity = 71
 3.9 Conclusion and Postscript = 72
4. Conceptual Clustering and Its Relation to Numerical Taxonomy / Douglas Fisher ; Pat Langley = 77
 4.1 Introduction = 77
 4.2 Numerical Taxonomy and Conceptual Clustering = 78
 4.3 More on Objects and Concepts = 86
 4.4 Some Conceptual Clustering Algorithms = 91
 4.5 Concluding Remarks = 113
5. Learning Rates in Supervised and Unsupervised Intelligent Systems / Stephen C. Hora = 117
 5.1 Introduction = 117
 5.2 Existing Results for Obtaining Learning Rates = 118
 5.3 Developing the Inefficiencies = 119
 5.4 Likelihood Theory = 121
 5.5 Comparing Supervised and Unsupervised Learning = 122
 5.6 Learning on Demand = 126
 5.7 Extensions to a Larger Problem Domain = 129
 5.8 Additional Topics = 129
6. Pinpointing Good Hypotheses with Heuristics / Steven Salzberg = 133
 6.1 Introduction = 133
 6.2 The Straightforward Approach : Statistics = 134
 6.3 Heuristics for Pruning Search = 139
 6.4 Rationalization = 153
 6.5 HANDICAPPER's Performance and Conclusions = 154
 6.6 Conclusions = 155
7. Artificial Intelligence Approaches in Statistics / Robert I. Phelps ; P. B. Musgrove = 159
 7.1 Introduction = 159
 7.2 AI Appfoaches in Statistics = 160
 7.3 Cluster Discrimination = 161
 7.4 Discussion = 170
8. REX Review / William A. Gale = 173
 8.1 Introduction = 173
 8.2 Summary = 174
 8.3 How REX Looks to the User = 176
 8.4 Why We Built REX = 194
 8.5 The Inference Engine = 201
 8.6 Strategy = 222
 8.7 Conclusions = 225
9. Representing Statistical Computations : Toward a Deeper Understanding / Thomas Ellman = 229
 9.1 Introduction = 229
 9.2 Representing Statistical Computations = 230
 9.3 Generating Explanations = 233
 9.4 Extensions to the Representation = 235
 9.5 Using the Representation to Aid Knowledge Acquisition = 237
 9.6 Conclusions = 238
10. Student Phase 1 - A Report on Work in Progress / William A. Gale = 239
 10.1 Overview = 239
 10.2 Acquiring and Using Initialization Knowledge = 246
 10.3 Learning How to Detect and Fix Problems = 253
 10.4 Inference Techniques = 257
 10.5 Control Modules = 262
11. Representing Statistical Knowledge for Expert Data Analysis Systems / Ronald A. Thisted = 267
 11.1 Groundwork : A Context for Expert Systems Research in Data Analysis = 267
 11.2 Knowledge Engineers, Statistical Consultants, and Computers = 273
 11.3 A Paradigm for Data Analysis = 275
 11.4 Strategies for Data Analysis = 278
 11.5 A Brief Note on the Semantic Map = 281
12. Environments for Supporting Statistical Strategy / Peter J. Huber = 285
 12.1 Background : The Data Analysis Paradigm = 285
 12.2 The Human-Human Interaction and Implications for System Design = 287
 12.3 The Data Analysis Environment = 287
 12.4 Analysis Sessions and Record Keeping = 288
 12.5 The Laboratory Assistant = 289
 12.6 Artificial Intelligence and Expert Systems = 291
13. Use of Psychometric Tools for Knowledge Acquisition : A Case Study / Keith A. Butler ; James E. Corter = 295
 13.1 Introduction = 295
 13.2 Knowledge Transfer Task Requirements = 296
 13.3 Measurement Models and Scaling Methods = 300
 13.4 Unidimensional Scaling = 300
 13.5 Case Study : Using EXTREE to Guide Feature Elicitation Interviews = 307
 13.6 Discussion = 313
14. The Analysis Phase in Development of Knowledge Based Systems / Annie G. Brooking = 321
 14.1 Introduction = 321
 14.2 Analysis = 323
 14.3 The Human Analysis Approach = 325
 14.4 The Knowledge Elicitation Phase = 326
 14.5 Phases in Knowledge Elicitation = 328
 14.6 The Knowledge Engineer = 331
 14.7 Conclusion = 333
15. Implementation and Study of Statistical Strategy / R. Wayne Oldford ; Stephen C. Peters = 335
 15.1 Introduction = 335
 15.2 The Problem = 336
 15.3 What Can Be Done? = 339
 15.4 Implementing Low-level Strategies = 341
 15.5 Implementing Higher-level Strategies = 344
 15.6 Evaluation = 348
 15.7 Summary and Concluding Remarks = 349
16. Patterns in Statistical Strategy / David J. Hand = 335
 16.1 Introduction = 356
 16.1 Types of Statistical Expert System = 357
 16.3 The Structure of the Strategy of Statistical Analysis = 358
 16.4 MANOVA Strategy = 364
 16.5 Discriminant Analysis = 371
 16.6 The Role of Artificial Intelligence Techniques = 373
 16.7 Conclusion = 375
  Appendix 1 : Examples of Interviews by Keyboard = 376
17. A DIY Guide to Statistical Strategy / Daryl Pregibon = 389
 17.1 Introduction = 389
 17.2 Some Do's and Don'ts of Strategy Development = 390
 17.3 Developing Your Strategy = 392
 17.4 Implementation = 395
 17.5 Epilogue = 399
18. An Alphabet for Statisticians' Expert Systems = 401
 18.1 A Is for All-Importance, Amelioration, and Areas = 401
 18.2 B Is for Branching = 402
 18.3 C Is for Cycles, Costs, Complexity, and Cheap Pie in the Sky = 403
 18.4 D Is for Difficulties, Dangers, Development, and Data-Dredging = 404
 18.5 E Is for Education = 406
 18.6 F Is for, Fix-It, Functionality, and the Future = 407