Likelihood-free inference via classification

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
15
1-15
Series
STATISTICS AND COMPUTING
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.
Description
Keywords
Approximate Bayesian computation, Generative models, Intractable likelihood, Latent variable models, Simulator-based models
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Citation
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