Estimation, Prediction, and Smoothing in Discrete Parameter Systems
- 1 December 1970
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Computers
- Vol. C-19 (12), 1193-1203
- https://doi.org/10.1109/t-c.1970.222858
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
This publication has 3 references indexed in Scilit:
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- Probabilistic automataInformation and Control, 1963
- New Results in Linear Filtering and Prediction TheoryJournal of Basic Engineering, 1961