Large Deviations for Discrete-Time Processes with Averaging

This book is mainly based on the Cramir–Chernoff renowned theorem, which deals with the ‘rough’ logarithmic asymptotics of the distribution of sums of independent, identically distributed random variables. The authors approach primarily the extensions of this theory to dependent, and in particular, nonmarkovian cases on function spaces. Recurrent algorithms of identification and adaptive control form the main examples behind the large deviation problems in this volume.
The first part of the book exploits some ideas and concepts of the martingale approach, especially the concept of the stochastic exponential. The second part of the book covers Freindlin’s approach, based on the Frobenius-type theorems for positive operators, which prove to be effective for the cases in consideration.