An epistemology of Bayes estimation

Wynn Stirling; Darryl Morrell

doi:10.1109/acssc.1989.1200987

An epistemology of Bayes estimation

Wynn Stirling, Darryl Morrell

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Bayesian estimation of the state of a system with unknown prior distribution is discussed. If the prior distribution lies in a convex set, then the posterior distribution also lies in a convex set. For linear Gaussian systems, the convex set may be generated by a set of Gaussian distributions with equal covariance with means in a convex region of state space. The Kalman filter is generalized to a set-valued Kalman filter, consisting of equations of evolution of a convex set of conditional means and a conditional covariance. An example is presented to illustrate and interpret the output of the estimator.

Original language	English (US)
Title of host publication	Twenty Third Annu Asilomar Conf Signal Syst Comput
Publisher	Publ by Maple Press, Inc
Pages	699-703
Number of pages	5
ISBN (Print)	0929029301
DOIs	https://doi.org/10.1109/acssc.1989.1200987
State	Published - 1989
Externally published	Yes
Event	Twenty-Third Annual Asilomar Conference on Signals, Systems & Computers - Pacific Grove, CA, USA Duration: Oct 30 1989 → Nov 1 1989

Publication series

Name	Conference Record - Asilomar Conference on Circuits, Systems & Computers
Volume	2

Conference

Conference	Twenty-Third Annual Asilomar Conference on Signals, Systems & Computers
City	Pacific Grove, CA, USA
Period	10/30/89 → 11/1/89

ASJC Scopus subject areas

General Engineering

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10.1109/acssc.1989.1200987

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Stirling, W & Morrell, D 1989, An epistemology of Bayes estimation. in Twenty Third Annu Asilomar Conf Signal Syst Comput. Conference Record - Asilomar Conference on Circuits, Systems & Computers, vol. 2, Publ by Maple Press, Inc, pp. 699-703, Twenty-Third Annual Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA, 10/30/89. https://doi.org/10.1109/acssc.1989.1200987

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AB - Bayesian estimation of the state of a system with unknown prior distribution is discussed. If the prior distribution lies in a convex set, then the posterior distribution also lies in a convex set. For linear Gaussian systems, the convex set may be generated by a set of Gaussian distributions with equal covariance with means in a convex region of state space. The Kalman filter is generalized to a set-valued Kalman filter, consisting of equations of evolution of a convex set of conditional means and a conditional covariance. An example is presented to illustrate and interpret the output of the estimator.

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ER -

An epistemology of Bayes estimation

Abstract

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