Archive for likelihood-free inference

focused Bayesian prediction

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on June 3, 2020 by xi'an

In this fourth session of our One World ABC Seminar, my friend and coauthor Gael Martin, gave an after-dinner talk on focused Bayesian prediction, more in the spirit of Bissiri et al. than following a traditional ABC approach.  because along with Ruben Loaiza-Maya and [my friend and coauthor] David Frazier, they consider the possibility of a (mild?) misspecification of the model. Using thus scoring rules à la Gneiting and Raftery. Gael had in fact presented an earlier version at our workshop in Oaxaca, in November 2018. As in other solutions of that kind, difficulty in weighting the score into a distribution. Although asymptotic irrelevance, direct impact on the current predictions, at least for the early dates in the time series… Further calibration of the set of interest A. Or the focus of the prediction. As a side note the talk perfectly fits the One World likelihood-free seminar as it does not use the likelihood function!

“The very premise of this paper is that, in reality, any choice of predictive class is such that the truth is not contained therein, at which point there is no reason to presume that the expectation of any particular scoring rule will be maximized at the truth or, indeed, maximized by the same predictive distribution that maximizes a different (expected) score.”

This approach requires the proxy class to be close enough to the true data generating model. Or in the word of the authors to be plausible predictive models. And to produce the true distribution via the score as it is proper. Or the closest to the true model in the misspecified family. I thus wonder at a possible extension with a non-parametric version, the prior being thus on functionals rather than parameters, if I understand properly the meaning of Π(Pθ). (Could the score function be misspecified itself?!) Since the score is replaced with its empirical version, the implementation is  resorting to off-the-shelf MCMC. (I wonder for a few seconds if the approach could be seen as a pseudo-marginal MCMC but the estimation is always based on the same observed sample, hence does not directly fit the pseudo-marginal MCMC framework.)

[Notice: Next talk in the series is tomorrow, 11:30am GMT+1.]

ABC in Svalbard [news #1]

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , on March 23, 2020 by xi'an

We [Julien and myself] are quite pleased to announce that

  • the scientific committee for the workshop has been gathered
  • the webpage for the workshop is now on-line (with a wonderful walrus picture whose author we alas cannot identify)
  • the workshop is now endorsed by both IMS and ISBA, which will handle registration (to open soon)
  • the reservation of hotel rooms will be handled by Hurtigruten Svalbard through the above webpage (this is important as we already paid deposit for a certain number of rooms)
  • we are definitely seeking both sponsors and organisers of mirror workshops in more populated locations

As an item of trivia, let me recall that Svalbard stands for the archipelago, while Spitsbergen is the name of the main island, where Longyearbyen is located. (In Icelandic, Svalbarði means cold rim or cold coast.)

MiMo2020

Posted in Statistics with tags , , , , , , , , on January 24, 2020 by xi'an

On 26 and 27 March 2020, the maths department of the Université of Rouen, Normandy, France, organizes a (free) workshop on mixture distributions. With the following speakers

    • Christophe Biernacki  (Laboratoire Paul Painlevé, Univ. Lille 1 et INRIA)
    • Vincent Brault (Laboratoire Jean Kuntzmann, Univ. Grenoble Alpes)
    • Gilles Celeux  (Laboratoire de Mathématiques d’Orsay, Univ. Paris Sud et INRIA)
    • Elisabeth Gassiat  (Laboratoire de Mathématiques d’Orsay, Univ. Paris Sud)
    • Van Hà Hoang  (Laboratoire de Mathématique Raphaël Salem, Univ. Rouen Normandie)
    • Hajo Holzmann  (Philipps-University Marburg, Germany)
    • Dimitri Karlis  (Department of Statistics, Athens University of Economics and Business, Greece)
    • Trung Tin Nguyen (LMNO, Univ. Caen Normandie)
    • Andrea Rau  (Département de Génétique Animale, INRA, Jouy en Josas)
    • Pierre Vandekerkhove  (Laboratoire d’Analyse et de Mathématiques Appliquées, Univ. Paris-Est Marne-la-Vallée)
    • Cinzia Viroli  (Department of Statistical Sciences, Universita di Bologna, Italia)

Unfortunately, since this is my former department, I will not be able to attend as I am taking part into the SIAM Conference on Uncertainty Quantification (UQ20), on the very same days. In a session on likelihood-free inference.