SimulatedAnnealingABC
Documentation for SimulatedAnnealingABC.jl.
This package provides different SimulatedAnnealingABC (SABC) algorithms for Approximate Bayesian Computation (ABC). Other terms that are sometimes used for ABC are simulation-based inference or likelihood-free inference. :
ABC is well-suited for models where evaluating the likelihood function $p(D \mid θ)$ is computationally expensive, but sampling from the likelihood is relatively easy. This is often true for stochastic models with unobserved random states $z$:
$p(D \mid θ) = \int p(D \mid z, θ) p(z) \, \text{d}z$
If $z$ is high-dimensional, the integration may become so computational expensive that conventional MCMC algorithms are no longer feasible.
Can you evaluate the probability density of your posterior? Can you write your model in Turing.jl? Then you should most likely not be using this or any other ABC package! Conventional MCMC algorithms will be much more efficient.
References
Albert, C., Künsch, H.R., Scheidegger, A., 2015. A simulated annealing approach to approximate Bayes computations. Statistics and computing 25, 1217–1232. https://doi.org/10.1007/s11222-014-9507-8