Author : Peter S. Maybeck
Publisher : Academic Press
Page : 311 pages
File Size : 14,72 MB
Release : 1982-08-25
Category : Mathematics
ISBN : 0080960030
This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
Author : Maybeck
Publisher : Academic Press
Page : 307 pages
File Size : 42,71 MB
Release : 1982-08-10
Category : Mathematics
ISBN : 0080956513
Stochastic Models: Estimation and Control: v. 2
Author : Maybeck
Publisher : Academic Press
Page : 445 pages
File Size : 20,90 MB
Release : 1979-07-17
Category : Mathematics
ISBN : 0080956505
Stochastic Models: Estimation and Control: v. 1
Author : Robert J Elliott
Publisher : Springer Science & Business Media
Page : 374 pages
File Size : 26,55 MB
Release : 2008-09-27
Category : Science
ISBN : 0387848541
As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.
Author : Torsten Söderström
Publisher : Springer Science & Business Media
Page : 410 pages
File Size : 49,9 MB
Release : 2002-07-26
Category : Mathematics
ISBN : 9781852336493
This comprehensive introduction to the estimation and control of dynamic stochastic systems provides complete derivations of key results. The second edition includes improved and updated material, and a new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.
Author : Jason L. Speyer
Publisher : SIAM
Page : 391 pages
File Size : 32,87 MB
Release : 2008-11-06
Category : Mathematics
ISBN : 0898716551
The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control. The book covers discrete- and continuous-time stochastic dynamic systems leading to the derivation of the Kalman filter, its properties, and its relation to the frequency domain Wiener filter aswell as the dynamic programming derivation of the linear quadratic Gaussian (LQG) and the linear exponential Gaussian (LEG) controllers and their relation to HÝsubscript 2¨ and HÝsubscript Ýinfinity¨¨ controllers and system robustness. This book is suitable for first-year graduate students in electrical, mechanical, chemical, and aerospace engineering specializing in systems and control. Students in computer science, economics, and possibly business will also find it useful.
Author : Peter S. Maybeck
Publisher :
Page : pages
File Size : 14,65 MB
Release : 2002
Category : Control theory
ISBN :
Author : Peter S. Maybeck
Publisher :
Page : pages
File Size : 47,17 MB
Release : 1982
Category :
ISBN : 9780124807020
Author : P. R. Kumar
Publisher : SIAM
Page : 371 pages
File Size : 25,16 MB
Release : 2015-12-15
Category : Mathematics
ISBN : 1611974259
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 control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
Author : Andreas Diekmann
Publisher : Academic Press
Page : 352 pages
File Size : 31,67 MB
Release : 2014-05-10
Category : Mathematics
ISBN : 1483266567
Stochastic Modelling of Social Processes provides information pertinent to the development in the field of stochastic modeling and its applications in the social sciences. This book demonstrates that stochastic models can fulfill the goals of explanation and prediction. Organized into nine chapters, this book begins with an overview of stochastic models that fulfill normative, predictive, and structural–analytic roles with the aid of the theory of probability. This text then examines the study of labor market structures using analysis of job and career mobility, which is one of the approaches taken by sociologists in research on the labor market. Other chapters consider the characteristic trends and patterns from data on divorces. This book discusses as well the two approaches of stochastic modeling of social processes, namely competing risk models and semi-Markov processes. The final chapter deals with the practical application of regression models of survival data. This book is a valuable resource for social scientists and statisticians.
