Likelihood-free methods are useful for parameter estimation of complex models with
intractable likelihood functions for which it is easy to simulate data. Such models are …

Likelihood-free inference methods typically make use of a distance between simulated and
real data. A common example is the maximum mean discrepancy (MMD), which has …

The 21st century has seen an enormous growth in the development and use of approximate
Bayesian methods. Such methods produce computational solutions to certain “intractable” …

Energy distance is a statistical distance between the distributions of random vectors, which
characterizes equality of distributions. The name energy derives from Newton's gravitational …

Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions
for stochastic models with intractable likelihood, by relying on model simulations. In …

The Bayesian statistical paradigm uses the language of probability to express uncertainty
about the phenomena that generate observed data. Probability distributions thus …

Probabilistic forecasting relies on past observations to provide a probability distribution for a
future outcome, which is often evaluated against the realization using a scoring rule. Here …

Simulation models often lack tractable likelihood functions, making likelihood-free inference
methods indispensable. Approximate Bayesian computation generates likelihood-free …

Bayesian Likelihood-Free Inference methods yield posterior approximations for simulator
models with intractable likelihood. Recently, many works trained neural networks to …

Abstract Simulation-Based Inference (SBI) is a common name for an emerging family of
approaches that infer the model parameters when the likelihood is intractable. Existing SBI …