Evolutionary algorithms in engineering and computer science: Recent advances in genetic algorithms, evolution strategies, evolutionary programming, genetic programming and industrial applications: Edited by K. Miettinen, P. Neittaanmäki, M. M. Mäkelä and J. Périaux. John Wiley & Sons, Ltd., Chicester. (1999). £60.00

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 117 KB
• Volume: 38
• Category: Article
• ISSN: 0898-1221
• DOI: 10.1016/s0898-1221(99)91189-6

✦ Synopsis

Preface (Clark Glymour). 1. An overview of the representation and discovery of causal relationships using Bayesian networks (Gregory F. Cooper). I. Causation, representation and prediction. 2. Prediction and experimental design with graphical causal models (Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, Stephen Fienberg and Elizabeth Slate). 3. Graphs, structural models, and causality (Judea Pearl). II. Search. 4. A Bayesian approach to causal discover (David Heckerman, Christopher Meek and Gregory F. Cooper). 5. Truth is among the best explanations: Finding causal explanations of conditional independence and dependence (Richard Scheines, Clark Glymour, Peter Spirtes, Christopher Meek and Thomas Richardson). 6. An algorithm for causal inference in the presence of latent variables and selection bias (Peter Spirtes, Christopher Meek and Thomas Richardson). 7. Automated discovery of linear feedback models (Thomas Richardson and Peter Spirtes). III. Controversy over search. 8. On the impossibility of inferring causation from association without background knowledge (James M. Robins and Larry Wasserman). 9. On the possibility of inferring causation from association without background knowledge (Clark Glymour, Peter Spirtes and Thomas Richardson). 10. Rejoinder to Glymour, Spirtes, and Richardson (James M. Robins and Larry Wasserman). 11. Response to rejoinder (Clark Glymour, Peter Spirtes and Thomas Richardson). IV. Estimating causal effects. 12. Testing and estimation of direct effects by reparameterizing directed acyclic graphs with structural nested models (James M. Robins). 13. A clinician's tool for analyzing noncompliance (David Maxwell Chickering and Judea Pearl). 14. Estimating latent causal influences: TETRAD II model selection and Bayesian parameter estimation (Richard Scheines). V. Scientific applications. 15. Exploring hypothesis space: Examples from organismal biology (Bill Shipley). 16. In-flight calibration of satellite ion composition data using artificial intelligence methods (Joakim Waldemark and Patrick Norqvist). 17. Causal modeling of spectral data: A new tool to study nonlinear processes (Ludwik Liszka). 18. Modeling corn exports and exchange rates with directed graphs and statistical loss functions (Derya G. Akleman, David A. Bessler and Diana M. Burton). 19. Causal inferences from databases: Why universities lose students (Marek J. Druzdzel and Clark Glymour). Index.