Description
Bayesian inference methods are widely used to analyse
observations in particle physics and cosmology, but they can
be extremely computationally demanding. Recent work in this
area has focussed on developing new methods for greatly
accelerating such analyses, in particular by using neural
networks and nested sampling methods. I will give a brief
outline of these approaches, which are generic in nature,
and illustrate their use in a cosmological case study.
