Likelihood-free inference via classification

Understand

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.