Estimation, Prediction, and Smoothing in Discrete Parameter Systems

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.