Name: Causal Models and Intelligent Data Management By Alex Gammerman
SKU: 9783540663287
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Causal Models and Intelligent Data Management Summary

Causal Models and Intelligent Data Management by Alex Gammerman

The need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new computational methods. This book presents new intelligent data management methods and tools, including new results from the field of inference. Leading experts also map out future directions of intelligent data analysis. This book will be a valuable reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry.

I. Causal Models.- 1. Statistics, Causality, and Graphs.- 1.1 A Century of Denial.- 1.2 Researchers in Search of a Language.- 1.3 Graphs as a Mathematical Language.- 1.4 The Challenge.- References.- 2. Causal Conjecture.- 2.1 Introduction.- 2.2 Variables in a Probability Tree.- 2.3 Causal Uncorrelatedness.- 2.4 Three Positive Causal Relations.- 2.5 Linear Sign.- 2.6 Causal Uncorrelatedness Again.- 2.7 Scored Sign.- 2.8 Tracking.- References.- 3. Who Needs Counterfactuals?.- 3.1 Introduction.- 3.1.1 Decision-Theoretic Framework.- 3.1.2 Unresponsiveness and Insensitivity.- 3.2 Counterfactuals.- 3.3 Problems of Causal Inference.- 3.3.1 Causes of Effects.- 3.3.2 Effects of Causes.- 3.4 The Counterfactual Approach.- 3.4.1 The Counterfactual Setting.- 3.4.2 Counterfactual Assumptions.- 3.5 Homogeneous Population.- 3.5.1 Experiment and Inference.- 3.6 Decision-Analytic Approach.- 3.7 Sheep and Goats.- 3.7.1 ACE.- 3.7.2 Neyman and Fisher.- 3.7.3 Bioequivalence.- 3.8 Causes of Effects.- 3.8.1 A Different Approach?.- 3.9 Conclusion.- References.- 4. Causality: Independence and Determinism.- 4.1 Introduction.- 4.2 Conclusion.- References.- II. Intelligent Data Management.- 5. Intelligent Data Analysis and Deep Understanding.- 5.1 Introduction.- 5.2 The Question: The Strategy.- 5.3 Diminishing Returns.- 5.4 Conclusion.- References.- 6. Learning Algorithms in High Dimensional Spaces.- 6.1 Introduction.- 6.2 SVM for Pattern Recognition.- 6.2.1 Dual Representation of Pattern Recognition.- 6.3 SVM for Regression Estimation.- 6.3.1 Dual Representation of Regression Estimation.- 6.3.2 SVM Applet and Software.- 6.4 Ridge Regression and Least Squares Methods in Dual Variables.- 6.5 Transduction.- 6.6 Conclusion.- References.- 7. Learning Linear Causal Models by MML Sampling.- 7.1 Introduction.- 7.2 Minimum Message Length Principle.- 7.3 The Model Space.- 7.4 The Message Format.- 7.5 Equivalence Sets.- 7.5.1 Small Effects.- 7.5.2 Partial Order Equivalence.- 7.5.3 Structural Equivalence.- 7.5.4 Explanation Length.- 7.6 Finding Good Models.- 7.7 Sampling Control.- 7.8 By-products.- 7.9 Prior Constraints.- 7.10 Test Results.- 7.11 Remarks on Equivalence.- 7.11.1 Small Effect Equivalence.- 7.11.2 Equivalence and Causality.- 7.12 Conclusion.- References.- 8. Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis.- References.- 9. On the Accuracy of Stochastic Complexity Approximations.- 9.1 Introduction.- 9.2 Stochastic Complexity and Its Applications.- 9.3 Approximating the Stochastic Complexity in the Incomplete Data Case.- 9.4 Empirical Results.- 9.4.1 The Problem.- 9.4.2 The Experimental Setting.- 9.4.3 The Algorithms.- 9.4.4 Results.- 9.5 Conclusion.- References.- 10. AI Modelling for Data Quality Control Xiaohui Liu.- 10.1 Introduction.- 10.2 Statistical Approaches to Outliers.- 10.3 Outlier Detection and Analysis.- 10.4 Visual Field Test.- 10.5 Outlier Detection.- 10.5.1 Self-Organising Maps (SOM).- 10.5.2 Applications of SOM.- 10.6 Outlier Analysis by Modelling 'Real Measurements'.- 10.7 Outlier Analysis by Modelling Noisy Data.- 10.7.1 Noise Model I: Noise Definition.- 10.7.2 Noise Model II: Construction.- 10.7.3 Noise Elimination.- 10.8 Concluding Remarks.- References.- 11. New Directions in Text Categorization.- 11.1 Introduction.- 11.2 Machine Learning for Text Classification.- 11.3 Radial Basis Functions and the Bard.- 11.4 An Evolutionary Algorithm for Text Classification.- 11.5 Text Classification by Vocabulary Richness.- 11.6 Text Classification with Frequent Function Words.- 11.7 Do Authors Have Semantic Signatures?.- 11.8 Syntax with Style.- 11.9 Intermezzo.- 11.10 Some Methods of Textual Feature-Finding.- 11.10.1 Progressive Pairwise Chunking.- 11.10.2 Monte Carlo Feature Finding.- 11.10.3 How Long Is a Piece of Substring?.- 11.10.4 Comparative Testing.- 11.11 Which Methods Work Best? - A Benchmarking Study.- 11.12 Discussion.- 11.12.1 In Praise of Semi-Crude Bayesianism.- 11.12.2 What's So Special About Linguistic Data?.- References.

Additional information

Sku

NPB9783540663287

ISBN 13

9783540663287

ISBN 10

3540663282

Title

Causal Models and Intelligent Data Management by Alex Gammerman

Author

Alex Gammerman

Condition

New

Binding type

Hardback

Publisher

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Year published

1999-08-19

Number of pages

185

Prizes

N/A

Cover note

Book picture is for illustrative purposes only, actual binding, cover or edition may vary.

Note

This is a new book - be the first to read this copy. With untouched pages and a perfect binding, your brand new copy is ready to be opened for the first time

Causal Models and Intelligent Data Management Alex Gammerman

Causal Models and Intelligent Data Management by Alex Gammerman

Summary

Causal Models and Intelligent Data Management Summary

Causal Models and Intelligent Data Management by Alex Gammerman

Table of Contents

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