approxbayescomp

Approximate Bayesian Computation (ABC) is a statistical method to fit a Bayesian model to data when the likelihood function is hard to compute. The `approxbayescomp` package implements an efficient form of ABC — the sequential Monte Carlo (SMC) algorithm. While it can handle any general statistical problem, we built in some models so that fitting insurance loss distributions is particularly easy.

References

Pierre-Olivier Goffard, Patrick J. Laub (2021), Approximate Bayesian Computations to fit and compare insurance loss models, Insurance: Mathematics and Economics, 100, pp. 350-371

See also

https://pat-laub.github.io/approxbayescomp-r/ and https://pat-laub.github.io/approxbayescomp/

Author

Patrick Laub <patrick.laub@gmail.com>