Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Understand

Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations.

• Typically, these forward models do not admit tractable densities forcing practitioners to make use of approximations.
• This work introduces a novel approach to address the intractability of the likelihood and the marginal model.
• We achieve this by learning a flexible amortized estimator which approximates the likelihood-to-evidence ratio.