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  • Statistical Learning Theory And Stochastic Optimization : Ecole D'ete De Prob...

    • Item No : 356831649559
    • Condition : Brand New
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    • Current Bid : US $79.15
    • * Item Description

    • Statistical Learning Theory And Stochastic Optimization : Ecole D'ete De Probabilites De Saint-flour Xxxi - 2001, Paperback by Catoni, Olivier; Picard, Jean (EDT), ISBN 3540225722, ISBN-13 9783540225720, Brand New, Free shipping in the US

      Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade th: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.

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