A BAYESIAN APPROACH TO PROBLEMS IN STOCHASTIC ESTIMATION AND CONTROL

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 09, 1964
Accession Number
AD0604006

Entities

People

  • Robert C. K. Lee
  • Yu-chi Ho

Organizations

  • Harvard University

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Bayesian Networks
  • Computations
  • Detectors
  • Equations
  • Filters
  • Markov Processes
  • Massachusetts
  • Mathematical Analysis
  • Measurement
  • Military Research
  • Monte Carlo Method
  • Noise
  • Probability
  • Regulators
  • Sequences
  • United States

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms