Estimation, Optimization & Analysis

A 'stochastic' process is a 'random' or - 'conjectural' process, and this book is concerned with applied probability and statistics.Whilst maintaining the mathematical rigour this subject requires, it, addresses topics of interest to engineers, such as problems in modelling, control, reliability maintenance, data analysis and engineering involvement with insurance.

The aim of this publication is to present important current tools used in the stochastic processes - estimation, optimization and recursive logarithms - in a form accessible to engineers (and that can also be applied to Matlab programs), and to gather together, in a unified presentation, many of the results in probability and statistics.

Amongst the themes covered in the chapters are mathematical expectation arising from increasing information patterns, the estimation of probability distribution, the treatment of distribution of real random phenomena (in engineering, economics, biology and medicine etc), and expectation maximization.The latter part of the book considers optimization algorithms, which can be used, for example, to help in the better utilization of resources, and stochastic approximation algorithms, which can provide prototype models in many practical applications.