Causal Models and Intelligent Data Management

1. Front Matter

   Pages I-X

   PDF

2. Casual Models

   1. Front Matter

      Pages 1-1

      PDF

   2. Statistics, Causality, and Graphs

      • J. Pearl

      Pages 3-16

   3. Causal Conjecture

      • Glenn Shafer

      Pages 17-32

   4. Who Needs Counterfactuals?

      • A. P. Dawid

      Pages 33-50

   5. Causality: Independence and Determinism

      • Nancy Cartwright

      Pages 51-63

3. Intelligent Data Management

   1. Front Matter

      Pages 65-65

      PDF

   2. Intelligent Data Analysis and Deep Understanding

      • David J. Hand

      Pages 67-80

   3. Learning Algorithms in High Dimensional Spaces

      • A. Gammerman, V. Vovk

      Pages 81-88

   4. Learning Linear Causal Models by MML Sampling

      • Chris S. Wallace, Kevin B. Korb

      Pages 89-111

   5. Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis

      • Y. E. Malashenko, N. M. Novikova, O. A. Vorobeichikova

      Pages 112-119

   6. On the Accuracy of Stochastic Complexity Approximations

      • Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri

      Pages 120-136

   7. AI Modelling for Data Quality Control

      • Xiaohui Liu

      Pages 137-150

   8. New Directions in Text Categorization

      • Richard S. Forsyth

      Pages 151-185

Data analysis and inference have traditionally been research areas of statistics. However, the need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new methods and tools, new types of databases, new efficient algorithms, new data structures, etc. - in effect new computational methods.
This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area.

• Apple
• Support Vector Machine
• Syntax
• algorithms
• classification
• cognition
• complexity
• computer science
• data analysis
• databases
• evolutionary algorithm
• learning
• machine learning
• modeling
• pattern recognition

• Department of Computer Science, University of London, Royal Holloway, UK

  Alex Gammerman

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