Speaker
Description
“Likelihood-Free inference allows scientists to perform traditional analyses such as parameter estimation and model comparison in situations where the explicit computation of a likelihood is impossible. Amongst all methods, Density Estimation LFI (DELFI) has excelled due to its efficient use of simulations.
However, despite its undeniable promise, current DELFI applications rely on a key approximation, which is the use of a point estimate density estimator. The goal of this work is, instead of finding the fastest methods available, to ask the question: “How far can one get using current computing power without making any compromises or approximations?” By doing this, we hope to gain a better understanding of the method, and develop the basis for future DELFI algorithms."
