Causal Models and Intelligent Data Mangement,
A.Gammerman (ed), Springer, 1999
Data analysis and inference have traditionally been research areas
in statistics. However, the need to handle large-scale, high-dimensional
data sets electronically requires the devlopments of new methods
and tools - of new computational methods and tools - thus
bringing the field into the realm of computer science.
In this book, leading experts map out the major trends and future
directions of intelligent data analysis, presenting new intelligent
data management and inferential tools and methods such as the support
vector machine and causal modelling. It is valuable not only to
those researchers exploring the interdisciplinary frontier between
statistics and computer science, but also to professionals applying
advanced methods in industry and commerce, and as an introduction
for students and lecturers new to this area.
Part I. Causal Models
- Statistics, Causality, and Graphs, J. Pearl.
- Causal Conjecture, Glenn Shafer.
- Who Needs Counterfactuals?, A.P. Dawid.
- Causality: Independence and Determinism, Nancy Cartwright.
Part II. intelligent Data Management
- Intelligent Data Analysis and Deep Understanding, David
- Learning Algorithms in High Dimensional Spaces, A. Gammerman
and V. Vovk.
- Learning Linear Causal Models by MML Sampling, Chris
S. Wallace and Kevin B. Korb.
- Game Theory Approach to Multicommodity Flow Network Vulnerability
Analysis, Y.E. Malashenko, N.M. Novikova and O.A. Vorobeichikova.
- On the Accuracy of Stochastic Complexity Approximations,
Petri Kontkanen, Petri Myllymäki, Tomi Silander and Henry
- AI Modelling for Data Quality Control, Xiaohui Liu.
- New Directions in Text Categorisation, Richard S. Forsyth.
Wed, 17-Jun-2009 15:57
Department of Computer Science,
University of London,
Surrey TW20 0EX
Tel/Fax : +44 (0)1784 443421