Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area.
This book provides:
Succinct and rigorous treatment of the foundations of stochastic control.
A unified approach to filtering, estimation, prediction, and stochastic and adaptive cools
The conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
