A BAYESIAN APPROACH TO PROBLEMS IN STOCHASTIC ESTIMATION AND CONTROL
HARVARD UNIV CAMBRIDGE MA CRUFT LAB
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A general class of stochastic estimation and control problems is formulated from the Bayesian Decision-Theoretic viewpoint. A discussion as to how these problems can be solved step-by-step in principle and practice from this approach is presented. As a specific example, the closed form Wiener- Kalman solution for linear estimation in gaussian noise is derived. The purpose of the paper is to show that the Bayesian approach provides i a general unifying framework within which to pursue further researches in stochastic estimation and control problems, ii the necessary computations and difficulties that must be overcome for these problems. An example of nonlinear, non-gaussian estimation problem is also solved.
- Statistics and Probability