Statistical learning theory and stochastic optimization

Description:

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'' (i.e. 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 the book: 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.

subjects: Congresses,  Mathematical statistics,  Probabilities,  Statistics,  Mathematical optimization,  Stochastic processes,  Probabilités,  Congrès,  Statistique mathématique,  Statistique,  Statistiek,  Stochastische methoden,  Optimaliseren,  Artificial intelligence,  Mathematics,  Numerical analysis,  Distribution (Probability theory)