Category: estimating constants

likelihood-free inference by ratio estimation

“This approach for posterior estimation with generative models mirrors the approach of Gutmann and Hyvärinen (2012) for the estimation of unnormalised models. The main difference is that here we classify between two simulated data sets while Gutmann and Hyvärinen (2012) classified between the observed data and simulated reference data.” A 2018 arXiv posting by Owen […]

Bayesian intelligence in Warwick

This is an announcement for an exciting CRiSM Day in Warwick on 20 March 2019: with speakers 10:00-11:00 Xiao-Li Meng (Harvard): “Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox” 11:00-12:00 Julien Stoehr (Dauphine): “Gibbs sampling and ABC” 14:00-15:00 Arthur Ulysse Jacot-Guillarmod (École Polytechnique Fedérale de Lausanne): “Neural Tangent Kernel: […]

Bayesian intelligence in Warwick

This is an announcement for an exciting CRiSM Day in Warwick on 20 March 2019: with speakers 10:00-11:00 Xiao-Li Meng (Harvard): “Artificial Bayesian Monte Carlo Integration: A Practical Resolution to the Bayesian (Normalizing Constant) Paradox” 11:00-12:00 Julien Stoehr (Dauphine): “Gibbs sampling and ABC” 14:00-15:00 Arthur Ulysse Jacot-Guillarmod (École Polytechnique Fedérale de Lausanne): “Neural Tangent Kernel: […]