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Pattern TheoryFrom representation to inference$
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Ulf Grenander and Michael I. Miller

Print publication date: 2006

Print ISBN-13: 9780198505709

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780198505709.001.0001

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The Bayes Paradigm, Estimation and Information Measures

The Bayes Paradigm, Estimation and Information Measures

Chapter:
(p.5) 2 The Bayes Paradigm, Estimation and Information Measures
Source:
Title Pages
Author(s):

Ulf Grenander

Michael I. Miller

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198505709.003.0003

The basic paradigm is the Bayesian setup, given is the source of parameters X ∈ X which are seen through a noisy channel giving observations Y ∈ Y. The posterior distribution determines the bounds on estimation of X given Y, the risk associated with estimating it, as well as a characterization of the information in the observation in Y about X.

Keywords:   complex random variables, emission tomography, inference engine, least squares estimation, maximum entropy models, noisy channels, point estimators, recognition, smoothness conditions, thresholding function

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