Causal Models and Intelligent Data Management

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. 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. Front Matter....Pages I-X Front Matter....Pages 1-1 Statistics, Causality, and Graphs....Pages 3-16 Causal Conjecture....Pages 17-32 Who Needs Counterfactuals?....Pages 33-50 Causality: Independence and Determinism....Pages 51-63 Front Matter....Pages 65-65 Intelligent Data Analysis and Deep Understanding....Pages 67-80 Learning Algorithms in High Dimensional Spaces....Pages 81-88 Learning Linear Causal Models by MML Sampling....Pages 89-111 Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis....Pages 112-119 On the Accuracy of Stochastic Complexity Approximations....Pages 120-136 AI Modelling for Data Quality Control....Pages 137-150 New Directions in Text Categorization....Pages 151-185 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.