Causal Models and Intelligent Data Management

By: Alex Gammerman (Editor)

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Hardcover | 19 August 1999

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Hardcover
200 Pages

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24.77 x 16.51 x 1.27

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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.

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Non-Fiction Mathematics Computing & I.T.Databases Data Capture & Analysis Computer Science Artificial Intelligence Pattern Recognition Information Retrieval

Causal Models
Statistics, Causality, and Graphs	p. 3
A Century of Denial	p. 3
Researchers in Search of a Language	p. 5
Graphs as a Mathematical Language	p. 8
The Challenge	p. 13
References	p. 14
Causal Conjecture	p. 17
Introduction	p. 17
Variables in a Probability Tree	p. 18
Causal Uncorrelatedness	p. 19
Three Positive Causal Relations	p. 20
Linear Sign	p. 22
Causal Uncorrelatedness Again	p. 26
Scored Sign	p. 27
Tracking	p. 28
References	p. 32
Who Needs Counterfactuals?	p. 33
Introduction	p. 33
Decision-Theoretic Framework	p. 33
Unresponsiveness and Insensitivity	p. 34
Counterfactuals	p. 35
Problems of Causal Inference	p. 36
Causes of Effects	p. 36
Effects of Causes	p. 36
The Counterfactual Approach	p. 37
The Counterfactual Setting	p. 37
Counterfactual Assumptions	p. 38
Homogeneous Population	p. 39
Experiment and Inference	p. 40
Decision-Analytic Approach	p. 43
Sheep and Goats	p. 45
ACE	p. 45
Neyman and Fisher	p. 45
Bioequivalence	p. 46
Causes of Effects	p. 47
A Different Approach?	p. 48
Conclusion	p. 48
References	p. 49
Causality: Independence and Determinism	p. 51
Introduction	p. 51
Conclusion	p. 61
References	p. 63
Intelligent Data Management
Intelligent Data Analysis and Deep Understanding	p. 67
Introduction	p. 67
The Question: The Strategy	p. 68
Diminishing Returns	p. 74
Conclusion	p. 78
References	p. 79
Learning Algorithms in High Dimensional Spaces	p. 81
Introduction	p. 81
SVM for Pattern Recognition	p. 82
Dual Representation of Pattern Recognition	p. 83
SVM for Regression Estimation	p. 84
Dual Representation of Regression Estimation	p. 84
SVM Applet and Software	p. 85
Ridge Regression and Least Squares Methods in Dual Variables	p. 86
Transduction	p. 87
Conclusion	p. 88
References	p. 88
Learning Linear Causal Models by MML Sampling	p. 89
Introduction	p. 89
Minimum Message Length Principle	p. 90
The Model Space	p. 92
The Message Format	p. 93
Equivalence Sets	p. 95
Small Effects	p. 96
Partial Order Equivalence	p. 97
Structural Equivalence	p. 97
Explanation Length	p. 98
Finding Good Models	p. 98
Sampling Control	p. 102
By-products	p. 102
Prior Constraints	p. 102
Test Results	p. 103
Remarks on Equivalence	p. 106
Small Effect Equivalence	p. 106
Equivalence and Causality	p. 107
Conclusion	p. 110
References	p. 110
Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis	p. 112
References	p. 118
On the Accuracy of Stochastic Complexity Approximations	p. 120
Introduction	p. 120
Stochastic Complexity and Its Applications	p. 122
Approximating the Stochastic Complexity in the Incomplete Data Case	p. 124
Empirical Results	p. 125
The Problem	p. 125
The Experimental Setting	p. 127
The Algorithms	p. 129
Results	p. 130
Conclusion	p. 132
References	p. 134
AI Modelling for Data Quality Control	p. 137
Introduction	p. 137
Statistical Approaches to Outliers	p. 137
Outlier Detection and Analysis	p. 139
Visual Field Test	p. 139
Outlier Detection	p. 141
Self-Organising Maps (SOM)	p. 141
Applications of SOM	p. 142
Outlier Analysis by Modelling `Real Measurements'	p. 143
Outlier Analysis by Modelling Noisy Data	p. 145
Noise Model I: Noise Definition	p. 145
Noise Model II: Construction	p. 146
Noise Elimination	p. 147
Concluding Remarks	p. 147
References	p. 148
New Directions in Text Categorization	p. 151
Introduction	p. 151
Machine Learning for Text Classification	p. 153
Radial Basis Functions and the Bard	p. 156
An Evolutionary Algorithm for Text Classification	p. 158
Text Classification by Vocabulary Richness	p. 161
Text Classification with Frequent Function Words	p. 163
Do Authors Have Semantic Signatures?	p. 164
Syntax with Style	p. 166
Intermezzo	p. 167
Some Methods of Textual Feature-Finding	p. 168
Progressive Pairwise Chunking	p. 169
Monte Carlo Feature Finding	p. 170
How Long Is a Piece of Substring?	p. 173
Comparative Testing	p. 175
Which Methods Work Best? - A Benchmarking Study	p. 177
Discussion	p. 180
In Praise of Semi-Crude Bayesianism	p. 180
What's So Special About Linguistic Data?	p. 180
References	p. 181
Table of Contents provided by Publisher. All Rights Reserved.

Causal Models and Intelligent Data Management

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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