The BOLFI method performs Likelihood-Free Inference by approximating the likelihood function with a Gaussian Process and greatly reduces the number of required evaluations by employing the Bayesian Optimization procedure to select new evaluation points.
The BOLFI method has been introduced in [1]. Similar approaches have been discussed in [2], [3].
See the documentation for more information.
[1] Michael U Gutmann, Jukka Cor, et al. “Bayesian optimization for likelihood-free inference of simulator-based statistical models”. In: Journal of Machine Learning Research 17.125 (2016), pp. 1–47.
[2] Bach Do and Makoto Ohsaki. “Bayesian optimization-assisted approximate Bayesian computation and its application to identifying cyclic constitutive law of structural steels”. In: Computers & Structures 286 (2023), p. 107111.
[3] Edward Meeds and Max Welling. “GPS-ABC: Gaussian process surrogate approxi- mate Bayesian computation”. In: arXiv preprint arXiv:1401.2838 (2014).