An easy-to-use empirical likelihood ABC method
This provides a more accessible tool for researchers in natural, engineering, and environmental sciences dealing with complex generative models, though it appears incremental as it builds on existing empirical likelihood ABC methods.
The authors tackled the computational intensity of Approximate Bayesian Computation (ABC) methods for models without analytical likelihoods by proposing an easy-to-use empirical likelihood ABC method that only requires summary statistics and simulation capabilities, showing that the posterior is consistent and exploring performance with examples.
Many scientifically well-motivated statistical models in natural, engineering and environmental sciences are specified through a generative process, but in some cases it may not be possible to write down a likelihood for these models analytically. Approximate Bayesian computation (ABC) methods, which allow Bayesian inference in these situations, are typically computationally intensive. Recently, computationally attractive empirical likelihood based ABC methods have been suggested in the literature. These methods heavily rely on the availability of a set of suitable analytically tractable estimating equations. We propose an easy-to-use empirical likelihood ABC method, where the only inputs required are a choice of summary statistic, it's observed value, and the ability to simulate summary statistics for any parameter value under the model. It is shown that the posterior obtained using the proposed method is consistent, and its performance is explored using various examples.