Nets and nests: accelerated Bayesian inference in astrophysics
March 25, 2013
Abstract: Bayesian inference methods are widely used to analyse observations in astrophysics and cosmology, but they can be extremely computationally demanding. Recent work in this area by the Cavendish Astrophysics Group has focussed on developing new methods for greatly accelerating such analyses, by up to a factor a million, using neural networks and nested sampling methods, such as the SkyNet and MultiNest packages respectively. These have recently been combined into the BAMBI algorithm, which accelerates Bayesian inference still further. I will give an outline of these approaches, which are generic in nature, and illustrate their use in a cosmological case study.