Diagnostics for Conditional Density Models and Bayesian Inference Algorithms

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

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

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, …

High-Dimensional density ratio estimation with extensions to approximate likelihood computation

The ratio between two probability density functions is an important component of various tasks, including selection bias correction, novelty detection and classification. Recently, several estimators of this ratio have been proposed. Most of these …