BOLFI.jl

BOLFI stands for "Bayesian Optimization Likelihood-Free Inference". BOLFI.jl provides a general algorithm, which uses the Bayesian optimization procedure to solve simulation-based inference problems. The package is inspired by the papers [1,2,3], which explored the idea of using Bayesian optimization for solving LFI problems.

The BOLFI method is based on Bayesian optimization. BOLFI.jl depends heavily on the BOSS.jl package which handles the underlying Bayesian optimization. As such, the BOSS.jl documentation can also be a useful resource when working with BOLFI.jl.

References

[1] Edward Meeds and Max Welling. “GPS-ABC: Gaussian process surrogate approximate Bayesian computation”. In: arXiv preprint arXiv:1401.2838 (2014).

[2] 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.

[3] 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.