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Basic model and notations
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Bayesian Field Theory Nonparametric
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Introduction
Contents
Bayesian framework
Subsections
Basic model and notations
Independent, dependent, and hidden variables
Energies, free energies, and errors
Posterior and likelihood
Predictive density
Mutual information and learning
Bayesian decision theory
Loss and risk
Loss functions for approximation
General loss functions and unsupervised learning
Maximum A Posteriori Approximation
Normalization, non-negativity, and specific priors
Empirical risk minimization
Interpretations of Occam's razor
A priori
information and
a posteriori
control
Joerg_Lemm 2001-01-21
