Exponential Family Models from Bayes' Theorem under Expectation Constraints
Data Analysis, Statistics and Probability
2016-05-02 v2
Abstract
It is shown that a consistent application of Bayesian updating from a prior probability density to a posterior using evidence in the form of expectation constraints leads to exactly the same results as the application of the maximum entropy principle, namely a posterior belonging to the exponential family. The Bayesian updating procedure presented in this work is not expressed as a variational principle, and does not involve the concept of entropy. Therefore it conceptually constitutes a complete alternative to entropic methods of inference.
Cite
@article{arxiv.1503.03451,
title = {Exponential Family Models from Bayes' Theorem under Expectation Constraints},
author = {Sergio Davis},
journal= {arXiv preprint arXiv:1503.03451},
year = {2016}
}