Causal Models and Intelligent Data Management
Springer Berlin Heidelberg, Aug 19, 1999 - Computers - 185 pages
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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Statistics Causality and Graphs
10 other sections not shown
Other editions - View all
algorithm applied approach Artificial Intelligence assume assumptions Bayesian network Causal Conjecture causal effects causal inference causal models causal relations causes clusterings coefficient Computer constraints counterfactual covariate data analysis data sets deterministic DIGRAMS distribution domain econometrics empirical equation error estimates example expected value Federalist Federalist Papers finite mixture function words given Glenn Shafer graph hypothesis identify independence condition input length linear regression linear sign Machine Learning marker mathematical means methods MML models model class multiflow Nature's tree Neural Networks nodes observed obtained optimal outliers parameters particular Pearl possible posterior probability prediction prior probability probabilistic probability tree problem Proc question random represent rule sample Science scored sign Section statistical step stochastic complexity approximation stochastic complexity measure structurally equivalent stylometry substrings techniques TETRAD text classification textual treatment unit variables