Likelihood-free Inference

Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning in Simulation and Uncertainty Quantification

Many areas of science make extensive use of computer simulators that implicitly encode likelihood functions for complex systems. Classical statistical methods are poorly suited for these so-called likelihood-free inference (LFI) settings, outside the …

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist. In these cases, it is sometimes necessary to estimate an approximate …

ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations

Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model. Despite the recent interest in ABC, …