Causal models and intelligent data management

Alex Gammerman

196 pages, parution le 01/01/1999

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Résumé

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. ContentsPart I. Causal Models

Statistics, Causality, and Graphs
Causal Conjecture
Who Needs Counterfactuals?
Causality: Independence and Determinism

Part II. Intelligent Data Management

Intelligent Data Analysis and Deep Understanding
Learning Algorithms in High Dimensional Spaces
Learning Linear Causal Models by MML Sampling
Game Theory Approach to Multicommodity Flow
Network Vulnerability Analysis
On the Accuracy of Stochastic Complexity Approximations
AI Modelling for Data Quality Control
New Directions in Text Categorization

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Alex Gammerman
Parution	01/01/1999
Nb. de pages	196
Format	16 x 24
Couverture	Relié
Poids	395g
Intérieur	Noir et Blanc
EAN13	9783540663287