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Causal Models and Intelligent Data Mangement, A.Gammerman (ed), Springer, 1999

Causal Models and Intelligent Data Management cover

Data analysis and inference have traditionally been research areas in statistics. However, the need to handle large-scale, high-dimensional data sets electronically requires the devlopments of new methods and tools - of new computational methods and tools - thus bringing the field into the realm of computer science.

In this book, leading experts map out the major trends and future directions of intelligent data analysis, presenting new intelligent data management and inferential tools and methods such as the support vector machine and causal modelling. It is valuable not only to those researchers exploring the interdisciplinary frontier between statistics and computer science, but also to professionals applying advanced methods in industry and commerce, and as an introduction for students and lecturers new to this area.


Part I. Causal Models

  1. Statistics, Causality, and Graphs, J. Pearl.
  2. Causal Conjecture, Glenn Shafer.
  3. Who Needs Counterfactuals?, A.P. Dawid.
  4. Causality: Independence and Determinism, Nancy Cartwright.

Part II. intelligent Data Management

  1. Intelligent Data Analysis and Deep Understanding, David J. Hand.
  2. Learning Algorithms in High Dimensional Spaces, A. Gammerman and V. Vovk.
  3. Learning Linear Causal Models by MML Sampling, Chris S. Wallace and Kevin B. Korb.
  4. Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis, Y.E. Malashenko, N.M. Novikova and O.A. Vorobeichikova.
  5. On the Accuracy of Stochastic Complexity Approximations, Petri Kontkanen, Petri Myllymäki, Tomi Silander and Henry Tirri.
  6. AI Modelling for Data Quality Control, Xiaohui Liu.
  7. New Directions in Text Categorisation, Richard S. Forsyth.

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