Learning from Data

1. Front Matter

   Pages i-xii

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2. Causality

   1. Front Matter

      Pages 1-1

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   2. Two Algorithms for Inducing Structural Equation Models from Data

      • Paul R. Cohen, Dawn E. Gregory, Lisa Ballesteros, Robert St. Amant

      Pages 3-12

   3. Using Causal Knowledge to Learn More Useful Decision Rules From Data

      • Louis Anthony Cox Jr.

      Pages 13-22

   4. A Causal Calculus for Statistical Research

      • Judea Pearl

      Pages 23-33

   5. Likelihood-based Causal Inference

      • Qing Yao, David Tritchler

      Pages 35-44

3. Inference and Decision Making

   1. Front Matter

      Pages 45-45

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   2. Ploxoma: Testbed for Uncertain Inference

      • H. Blau

      Pages 47-57

   3. Solving Influence Diagrams Using Gibbs Sampling

      • Ali Jenzarli

      Pages 59-68

   4. Modeling and Monitoring Dynamic Systems by Chain Graphs

      • Alberto Lekuona, Beatriz Lacruz, Pilar Lasala

      Pages 69-77

   5. Propagation of Gaussian belief functions

      • Liping Liu

      Pages 79-88

   6. On Test Selection Strategies for Belief Networks

      • David Madigan, Russell G. Almond

      Pages 89-98

   7. Representing and Solving Asymmetric Decision Problems Using Valuation Networks

      • Prakash P. Shenoy

      Pages 99-108

   8. A Hill-Climbing Approach for Optimizing Classification Trees

      • Xiaorong Sun, Steve Y. Chiu, Louis Anthony Cox

      Pages 109-117

4. Search Control in Model Hunting

   1. Front Matter

      Pages 119-119

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   2. Learning Bayesian Networks is NP-Complete

      • David Maxwell Chickering

      Pages 121-130

   3. Heuristic Search for Model Structure: the Benefits of Restraining Greed

      • John F. Elder IV

      Pages 131-142

   4. Learning Possibilistic Networks from Data

      • Jörg Gebhardt, Rudolf Kruse

      Pages 143-153

   5. Detecting Imperfect Patterns in Event Streams Using Local Search

      • Adele E. Howe

      Pages 155-164

   6. Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

      • Pedro Larrañaga, Roberto Murga, Mikel Poza, Cindy Kuijpers

      Pages 165-174

Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.

• Bayesian network
• Likelihood
• artificial intelligence
• control
• data analysis
• decision problem
• decision tree
• genetic algorithms
• intelligence
• knowledge
• knowledge discovery
• learning
• modeling
• natural language
• natural language processing

• Department of Computer Science, Vanderbilt University, Nashville, USA

  Doug Fisher

• Department of Economics Institute of Statistics and Econometrics, Free University of Berlin, Berlin, Germany

  Hans-J. Lenz

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