all projects tagged Bayes-inference

sampling methods and physics of MCMC


Monte-Carlo Markov-chain methods are widely (and wildly) used in cosmological inference and can always be mapped back onto a canonical, statistical physics system. Interesting questions we pursue concern canonical parition functions reflecting cosmological, non-Gaussian likelihoods and analytical methods for inference for these cases.

machine learning in cosmology and beyond


Machine learning methods can help infering fundamental laws of Nature from complex data or to design inference processes that are otherwise difficult to manage. We are trying to apply inference on inflationary potentials with machine learning methods and hopefully establish links to information geometry.