Although no-one is, probably, too enthused about the idea, it is a fact that the development of most empirical sciences to a great extend depends of the development of data analysis methods and techniques, which, due to the necessity of applications of computers for that pur pose, actually means that it practically depends on the advancements and orientation of computational statistics. This volume contains complete texts of the lectures held during the Summer School on "Computational Aspects of Model Choice" orga nized jointly by Charles University, Prague, and International Associa tion for Statistical Computing (IASC) on July 1-14, 1991, in Prague. Main aims of the Summer School were to review and analyse some of the recent developments concerning computational aspects of the model choice as well as their theoretical background. The topics covers the problems of the change point detection, robust estimation and its computational aspects, classification using binary trees, stochastic ap proximation and optimization including the discussion about available software, computational aspects of graphical model selection and mul tiple hypotheses testing. The bridge between these different approaches is formed by the survey paper about statistical applications of artificial intelligence.
Inhaltsverzeichnis
Tomáš Havránek. - Bernd Streitberg. - Change Point Problem. - Robust Estimation in Linear Model and its Computational Aspects. - Constructing Prediction Trees from Data: The RECPAM Approach. - Stochastic Approximation and Optimization. - Comparison of the Stochastic Approximation Software. - Models, Algorithms and Software of Stochastic Optimization. - Some Computational Aspects of Graphical Model Selection. - Multiple Hypotheses Testing. - Statistical Applications of Artificial Intelligence.
