Stochastic Models: Estimation and Control: v. 2

Stochastic Models: Estimation and Control: v. 2
Author :
Publisher : Academic Press
Total Pages : 307
Release :
ISBN-10 : 9780080956510
ISBN-13 : 0080956513
Rating : 4/5 (10 Downloads)

Book Synopsis Stochastic Models: Estimation and Control: v. 2 by : Maybeck

Download or read book Stochastic Models: Estimation and Control: v. 2 written by Maybeck and published by Academic Press. This book was released on 1982-08-10 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Models: Estimation and Control: v. 2


Stochastic Models: Estimation and Control: v. 2 Related Books

Stochastic Models: Estimation and Control: v. 2
Language: en
Pages: 307
Authors: Maybeck
Categories: Mathematics
Type: BOOK - Published: 1982-08-10 - Publisher: Academic Press

DOWNLOAD EBOOK

Stochastic Models: Estimation and Control: v. 2
Stochastic Models, Estimation, and Control
Language: en
Pages: 311
Authors: Peter S. Maybeck
Categories: Mathematics
Type: BOOK - Published: 1982-08-25 - Publisher: Academic Press

DOWNLOAD EBOOK

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 fu
Stochastic Models: Estimation and Control: v. 1
Language: en
Pages: 445
Authors: Maybeck
Categories: Mathematics
Type: BOOK - Published: 1979-07-17 - Publisher: Academic Press

DOWNLOAD EBOOK

Stochastic Models: Estimation and Control: v. 1
Hidden Markov Models
Language: en
Pages: 374
Authors: Robert J Elliott
Categories: Science
Type: BOOK - Published: 2008-09-27 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working
Stochastic Systems
Language: en
Pages: 371
Authors: P. R. Kumar
Categories: Mathematics
Type: BOOK - Published: 2015-12-15 - Publisher: SIAM

DOWNLOAD EBOOK

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 engin