Random iterative models

The recent development of computation and automation has led to quick advances in the theory and practice of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multi-access broadcast channels, self-learning of neural networks...). This book provides a wide-angle view of those methods: stochastic approximation, linear and non-linear models, controlled Markov chains, estimation and adaptive control, learning... Mathematicians familiar with the basics of Probability and Statistics will find here a self-contained account of many approaches to those theories, some of them classical, some of them leading up to current and future research. Each chapter can form the core material for lectures on stochastic processes. Engineers having to control complex systems will find here algorithms with good performances and reasonably easy computation.