## Archive for Max Planck Institute

## black holes capture Nobel

Posted in Statistics, Travel, University life with tags astronomy, Astrophysics, black holes, Garching, Max Planck Institute, Milky Way, Nobel Prize, relativity on October 7, 2020 by xi'an## state of the art in sampling & clustering [workshop]

Posted in Books, pictures, Statistics, Travel, University life with tags Autrans, clustering, Garching, Germany, HMC, Max Planck Institute, Max-Planck-Institut für Physik, München, nested sampling, sampling, workshop on September 17, 2020 by xi'an**N**ext month, I am taking part in a workshop on sampling & clustering at the Max-Planck-Institut für Physik in Garching, Germany (near München). By giving a three hour introduction to ABC, as I did three years ago in Autrans. Being there and talking with local researchers if the sanitary conditions allow. From my office otherwise. Other speakers include Michael Betancourt on HMC and Johannes Buchner on nested sampling. The remote participation to this MPI workshop is both open and free, **but participants must register before 18 September, namely tomorrow.**

## news from PCI

Posted in Books, pictures, University life with tags alternative publishing, arXiv, bioRxiv, INRA, INRAE, Max Planck Institute, Muséum National d’Histoire Naturelle, open and free access, PCI, Peer Community, peer review, reproducibility on May 6, 2020 by xi'an## Max Ent at Max Plank

Posted in Statistics with tags Bayesian inference, Carl Friedrich Gauss, conference, Gauß, Germany, Max Planck Institute, MaxEnt 2019, maximum entropy, München, O'Bayes 2019, University of Warwick on December 21, 2018 by xi'an## a come-back of the harmonic mean estimator

Posted in Statistics with tags Alan Gelfand, Bayes factors, Bayesian computing, harmonic mean estimator, Max Planck Institute, München, Werner-Heisenberg-Institut on September 6, 2018 by xi'an**A**re we in for a return of the harmonic mean estimator?! Allen Caldwell and co-authors arXived a new document that Allen also sent me, following a technique that offers similarities with our earlier approach with Darren Wraith, the difference being in the more careful and practical construct of the partition set and use of multiple hypercubes, which is the smart thing. I visited Allen’s group at the Max Planck Institut für Physik (Heisenberg) in München (Garching) in 2015 and we confronted our perspectives on harmonic means at that time. The approach followed in the paper starts from what I would call the canonical Gelfand and Dey (1995) representation with a uniform prior, namely that the integral of an arbitrary non-negative function [or unnormalised density] ƒ can be connected with the integral of the said function ƒ over a smaller set Δ with a finite measure measure [or volume]. And therefore to simulations from the density ƒ restricted to this set Δ. Which can be recycled by the harmonic mean identity towards producing an estimate of the integral of ƒ over the set Δ. When considering a partition, these integrals sum up to the integral of interest but this is not necessarily the only exploitation one can make of the fundamental identity. The most novel part stands in constructing an adaptive partition based on the sample, made of hypercubes obtained after whitening of the sample. Only keeping points with large enough density and sufficient separation to avoid overlap. (I am unsure a genuine partition is needed.) In order to avoid selection biases the original sample is separated into two groups, used independently. Integrals that stand too much away from the others are removed as well. This construction may sound a bit daunting in the number of steps it involves and in the poor adequation of a Normal to an hypercube or conversely, but it seems to shy away from the number one issue with the basic harmonic mean estimator, the almost certain infinite variance. Although it would be nice to be completely certain this doom is avoided. I still wonder at the degenerateness of the approximation of the integral with the dimension, as well as at other ways of exploiting this always fascinating [if fraught with dangers] representation. And comparing variances.