Archive for webinar

ABC in Svalbard [#1]

Posted in Books, Mountains, pictures, R, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , on April 13, 2021 by xi'an

It started a bit awkwardly for me as I ran late, having accidentally switched to UK time the previous evening (despite a record-breaking biking-time to the University!), then the welcome desk could not find the key to the webinar room and I ended up following the first session from my office, by myself (and my teapot)… Until we managed to reunite in the said room (with an air quality detector!).

Software sessions are rather difficult to follow and I wonder what the idea on-line version should be. We could borrow from our teaching experience new-gained from the past year, where we had to engage students without the ability to roam the computer lab and look at their screens to force engage them into coding. It is however unrealistic to run a computer lab, unless a few “guinea pigs” could be selected in advance and show their progress or lack thereof during the session. In any case, thanks to the speakers who made the presentations of

  1. BSL(R)
  2. ELFI (Python)
  3. ABCpy (Python)

this morning/evening. (Just taking the opportunity to point out the publication of the latest version of DIYABC!).

Florence Forbes’ talk on using mixture of experts was quite alluring (and generated online discussions during the break, recovering some of the fun in real conferences), esp. from my longtime interest normalising flows in mixtures of regression (and more to come as part of our biweekly reading group!). Louis talked about gaining efficiency by not resampling the entire data in large network models. Edwin Fong brought martingales and infinite dimension distributions to the rescue, generalising Polya urns! And Justin Alsing discussed the advantages of estimating the likelihood rather than estimating the posterior, which sounds counterintuitive. With a return to mixtures as approximations, using instead normalising flows. With the worth-repeating message that ABC marginalises over nuisance parameters so easily! And a nice perspective on ABayesian decision, which does not occur that often in the ABC literature. Cecilia Viscardi made a link between likelihood estimation and large deviations à la Sanov, the rare event being associated with the larger distances, albeit dependent on a primary choice of the tolerance. Michael Gutmann presented an intringuing optimisation Monte Carlo approach from his last year AISTATS 2020 paper, the simulated parameter being defined by a fiducial inversion. Reweighted by the prior times a Jacobian term, which stroke me as a wee bit odd, ie using two distributions on θ. And Rito concluded the day by seeking approximate sufficient statistics by constructing exponential families whose components are themselves parameterised as neural networks with neural parameter ω. Leading to an unnormalised model because of the energy function, hence to the use of inference techniques on ω that do not require the constant, like Gutmann & Hyvärinen (2012). And using the (pseudo-)sufficient statistic as ABCsummary statistic. Which still requires an exchange MCMC step within ABC.

ABC in Svalbard [#0]

Posted in Statistics with tags , , , , , , , , , , , , , , , , on April 12, 2021 by xi'an

It has started! In case you need the link to follow the talks, here it is! And most slides and past talks are also on-line.

Fisher, Bayes, and predictive Bayesian inference [seminar]

Posted in Statistics with tags , , , , , , , , , on April 4, 2021 by xi'an

An interesting Foundations of Probability seminar at Rutgers University this Monday, at 4:30ET, 8:30GMT, by Sandy Zabell (the password is Angelina’s birthdate):

R. A. Fisher is usually perceived to have been a staunch critic of the Bayesian approach to statistics, yet his last book (Statistical Methods and Scientific Inference, 1956) is much closer in spirit to the Bayesian approach than the frequentist theories of Neyman and Pearson.  This mismatch between perception and reality is best understood as an evolution in Fisher’s views over the course of his life.  In my talk I will discuss Fisher’s initial and harsh criticism of “inverse probability”, his subsequent advocacy of fiducial inference starting in 1930, and his admiration for Bayes expressed in his 1956 book.  Several of the examples Fisher discusses there are best understood when viewed against the backdrop of earlier controversies and antagonisms.

news from ISBA

Posted in Kids, pictures, Statistics, University life with tags , , , , , , , , , , on March 31, 2021 by xi'an

Some news and reminders from the latest ISBA Bulletin (which also contains an obituary of Don Fraser by Christian Genest):

  • Remember that the registration for ISBA 2021 is free till 1 May! The conference is fully online, from 28 June to 2 July
  • the Bayesian young statisticians meeting BAYSM 21 will take place online, 1-3 September
  • the useR! 2021 conference will also take place online, July 5-9
  • the MHC2021 (Mixtures, Hidden Markov models, Clustering) conference will take place physically and online at Orsay, France, 2-4 June

sampling with neural networks [seminar]

Posted in Statistics with tags , , , , , on March 29, 2021 by xi'an

Tomorrow (30 March, 11am ET, 16 GMT, 17 CET) Grant Rotskoff will give a webinar on Sampling with neural networks: prospects and perils, with links to developments in generative modeling to sample distributions that are challenging to sample with local dynamics, and the perils of neural network driven sampling to accelerate sampling.

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