Bayesian optimisation for likelihood-free cosmological inference

Understand

Many cosmological models have only a finite number of parameters of interest, but a very expensive data-generating process and an intractable likelihood function.

• We address the problem of performing likelihood-free Bayesian inference from such black-box simulation-based models, under the constraint of a very limited simulation budget (typically a few thousand).
• To do so, we adopt an approach based on the likelihood of an alternative parametric model.
• Conventional approaches to approximate Bayesian computation such as likelihood-free rejection sampling are impractical for the considered problem, due to the lack of knowledge about how the parameters affect the discrepancy between observed and simulated data.