The Bayes Paradigm, Estimation and Information Measures
The Bayes Paradigm, Estimation and Information Measures
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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