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Learning functions and approximate Bayesian computation design: ABCD

Hainy, M. and Müller, W.G and Wynn, Henry P. (2014) Learning functions and approximate Bayesian computation design: ABCD. Entropy, 16 (8). pp. 4353-4374. ISSN 1099-4300

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Identification Number: 10.3390/e16084353

Abstract

Interventions aimed at high-need families have difficulty demonstrating short-term impact on child behaviour. 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.

Item Type: Article
Official URL: http://www.mdpi.com/journal/entropy
Additional Information: © 2014 Authors, licensee MDPI, Basel, Switzerland © CC BY 3.0
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Sets: Research centres and groups > Decision Support and Risk Group (DSRG)
Date Deposited: 05 Sep 2014 08:56
Last Modified: 15 Oct 2014 08:53
Projects: I-833-N18
Funders: French Science Fund (ANR), Austrian Science Fund (FWF), Exzellenzstipendium des Landes Oberösterreich
URI: http://eprints.lse.ac.uk/id/eprint/59283

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