Likelihood-free inference via classification

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http://hdl.handle.net/10138/231875

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Gutmann , M U , Dutta , R , Kaski , S & Corander , J 2018 , ' Likelihood-free inference via classification ' , Statistics and Computing , vol. 28 , no. 2 , pp. 411-425 . https://doi.org/10.1007/s11222-017-9738-6

Title: Likelihood-free inference via classification
Author: Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka
Contributor organization: Helsinki Institute for Information Technology
Department of Mathematics and Statistics
Jukka Corander / Principal Investigator
Date: 2018-03
Language: eng
Number of pages: 15
Belongs to series: Statistics and Computing
ISSN: 0960-3174
DOI: https://doi.org/10.1007/s11222-017-9738-6
URI: http://hdl.handle.net/10138/231875
Abstract: Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers.
Subject: Approximate Bayesian computation
Generative models
Intractable likelihood
Latent variable models
Simulator-based models
APPROXIMATE BAYESIAN COMPUTATION
UNNORMALIZED STATISTICAL-MODELS
NATURAL IMAGE STATISTICS
CHAIN MONTE-CARLO
STREPTOCOCCUS-PNEUMONIAE
SIMULATION
MACHINE
112 Statistics and probability
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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