Title :
Estimation, Prediction, and Smoothing in Discrete Parameter Systems
Author :
Booth, Taylor L.
Author_Institution :
IEEE
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.;
Journal_Title :
Computers, IEEE Transactions on
DOI :
10.1109/T-C.1970.222858
