Entropy | |
Learning Functions and Approximate Bayesian Computation Design: ABCD | |
Markus Hainy1  Werner G. Müller1  | |
[1] Department of Applied Statistics, Johannes Kepler University, 4040 Linz, Austria; E-Mails: | |
关键词: learning; Shannon information; majorization; optimum experimental design; approximate Bayesian computation; | |
DOI : 10.3390/e16084353 | |
来源: mdpi | |
【 摘 要 】
A general approach to Bayesian learning revisits some classical results, which study which functionals on a prior distribution are expected to increase, in a preposterior sense. The results are applied to information functionals of the Shannon type and to a class of functionals based on expected distance. A close connection is made between the latter and a metric embedding theory due to Schoenberg and others. For the Shannon type, there is a connection to majorization theory for distributions. A computational method is described to solve generalized optimal experimental design problems arising from the learning framework based on a version of the well-known approximate Bayesian computation (ABC) method for carrying out the Bayesian analysis based on Monte Carlo simulation. Some simple examples are given.
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland
【 预 览 】
Files | Size | Format | View |
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RO202003190023078ZK.pdf | 215KB | download |