DocumentCode
1104133
Title
Estimation, Prediction, and Smoothing in Discrete Parameter Systems
Author
Booth, Taylor L.
Author_Institution
IEEE
Issue
12
fYear
1970
Firstpage
1193
Lastpage
1203
Abstract
Deterministic and probabilistic sequential machine theory is used to solve the estimation, prediction, and smoothing problem encountered in noisy discrete parameter systems such as digital data processors and information processing systems. Using Bayes´ theorem, the equations describing the ideal estimator, predictor, and smoother are developed. These equations are used to define an infinite-state Mealy-type sequential machine that performs these calculations.
Keywords
Bayes´ estimation, estimation, machine approximation, prediction, probabilistic sequential machines, sequential machines.; Automata; Boats; Equations; Filtering theory; Information processing; Markov processes; Sampled data systems; Sequential analysis; Smoothing methods; Underwater vehicles; Bayes´ estimation, estimation, machine approximation, prediction, probabilistic sequential machines, sequential machines.;
fLanguage
English
Journal_Title
Computers, IEEE Transactions on
Publisher
ieee
ISSN
0018-9340
Type
jour
DOI
10.1109/T-C.1970.222858
Filename
1671451
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