Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with applications in several branches of engineering and in areas of the social sciences concerned with policy analysis and prescription. With the increase in computational capacity and the ability to collect and process huge quantities of data, an explosion of work in the area has been engendered. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, learning, and robotics. It is ideal for students previously acquainted with probability theory and stochastic processes, who wish to learn more on decision making with uncertainty, and can be used as a course textbook for advanced undergraduate or first year graduate students.
Preface to the classics edition; Preface; 1. Introduction; 2. State space models; 3. Properties on linear stochastic systems; 4. Controlled Markov chain model; 5. Input output models; 6. Dynamic programming; 7. Linear systems: estimation and control; 8. Infinite horizon dynamic programming; 9. Introduction to system identification; 10. Linear system identification; 11. Bayesian adaptive control; 12. Non-Bayesian adaptive control; 13. Self-tuning regulators for linear systems; References; Author index; Subject index.
