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Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.
The ExoplanetsSysSim.jl package generates populations of planetary systems, simulates observations of those systems with a transit survey, and facilitates comparisons of simulated and observed catalogs of planetary systems. Critically, ExoplanetsSysSim accounts for intrinsic correlations in the sizes and orbital periods of planets within a planetary system.