Category: Monte Carlo integration

what if what???

[Here is a section of the Wikipedia page on Monte Carlo methods which makes little sense to me. What if it was not part of this page?!] Monte Carlo simulation versus “what if” scenarios There are ways of using probabilities that are definitely not Monte Carlo simulations – for example, deterministic modeling using single-point estimates. […]

a new rule for adaptive importance sampling

Art Owen and Yi Zhou have arXived a short paper on the combination of importance sampling estimators. Which connects somehow with the talk about multiple estimators I gave at ESM last year in Helsinki. And our earlier AMIS combination. The paper however makes two important assumptions to reach optimal weighting, which is inversely proportional to […]

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: […]