Likelihood-free inference via classification

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Edinburgh
  • Università della Svizzera italiana
  • University of Oslo
  • University of Helsinki

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.

Details

Original languageEnglish
Pages (from-to)411–425
Number of pages15
JournalSTATISTICS AND COMPUTING
Volume28
Issue number2
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed

    Research areas

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

Download statistics

No data available

ID: 11411077