Speaker: Fahran Feroz, Cambridge
Abstract: Astrophysics and cosmology have increasingly become data driven with the availability of large amount of high quality data from missions like SDSS, Planck and LHC. This has resulted in Bayesian inference methods being widely used to analyse observations, but they can be extremely computationally demanding. My research over the past few years has focused on the development of 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. I will give an outline of these approaches, which are generic in nature, and illustrate their use in some interesting problems in astrophysics.