Likelihood-free inference via classification

Michael U. Gutmann*, Ritabrata Dutta, Samuel Kaski, Jukka Corander

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

27 Citations (Scopus)
256 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)411–425
Number of pages15
Issue number2
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • Approximate Bayesian computation
  • Generative models
  • Intractable likelihood
  • Latent variable models
  • Simulator-based models

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