Archive for Laplace’s Demon

how can a posterior be uniform?

Posted in Books, Statistics with tags , , , , , , on September 1, 2020 by xi'an

A bemusing question from X validated:

How can we have a posterior distribution that is a uniform distribution?

With the underlying message that a uniform distribution does not depend on the data, since it is uniform! While it is always possible to pick the parameterisation a posteriori so that the posterior is uniform, by simply using the inverse cdf transform, or to pick the prior a posteriori so that the prior cancels the likelihood function, there exist more authentic discrete examples of a data realisation leading to a uniform distribution, as eg in the Multinomial model. I deem the confusion to stem from the impression either that uniform means non-informative (what we could dub Laplace’s daemon!) or that it could remain uniform for all realisations of the sampled rv.

my demonic talk

Posted in Statistics with tags , , , , , , , , , , , on May 13, 2020 by xi'an

Laplace’s Demon [coming home!]

Posted in Kids, Linux, pictures, Statistics, University life with tags , , , , , , , , , , , , , on May 11, 2020 by xi'an

A new online seminar is starting this week, called Laplace’s Demon [after too much immersion in His Dark Materials, lately, ather than Unix coding, I first wrote daemon!] and concerned with Bayesian Machine Learning at Scale. Run by Criteo in Paris (hence the Laplace filiation, I presume!). Here is the motivational blurb from their webpage

Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. In this seminar series we ask distinguished speakers to comment on what role Bayesian statistics and Bayesian machine learning have in this rapidly changing landscape. Do we need to optimally process information or borrow strength in the big data era? Are philosophical concepts such as coherence and the likelihood principle relevant when you are running a large scale recommender system? Are variational approximations, MCMC or EP appropriate in a production environment? Can I use the propensity score and call myself a Bayesian? How can I elicit a prior over a massive dataset? Is Bayes a reasonable theory of how to be perfect but a hopeless theory of how to be good? Do we need Bayes when we can just A/B test? What combinations of pragmatism and idealism can be used to deploy Bayesian machine learning in a large scale live system? We ask Bayesian believers, Bayesian pragmatists and Bayesian skeptics to comment on all of these subjects and more.

The seminar takes places on the second Wednesday of the month, at 5pm (GMT+2) starting ill-fatedly with myself on ABC-Gibbs this very Wednesday (13 May 2020), followed by Aki Vehtari, John Ormerod, Nicolas Chopin, François Caron, Pierre Latouche, Victor Elvira, Sara Filippi, and Chris Oates. (I think my very first webinar was a presentation at the Deutsche Bank, New York, I gave from CREST videoconference room from 8pm till midnight after my trip was cancelled when the Twin Towers got destroyed, on 07 September 2001…)

MCMskv #1 [room with a view]

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on January 6, 2016 by xi'an

That’s it!, MCMskv has now started! We hold our round-table Monday night, which ended with most of my interventions revolving about the importance of models. And of the fact that models are always approximate (and wrong), hence that uncertainty and uncertainty ascertainment is paramount. Even more with large datasets and roundtablehigh-dimensional models. Apologies to the audience if I sounded like running on a very short loop. (And maybe also for the round-table to keep them from their dinner!)  Still, I got some items for reflection out of this discussion, including the notion that big data is usually and inappropriately associated with an impression of completeness that is almost deterministic in a Laplacian sense. Namely that the available data for, say, all Facebook users, seems to allow us (or The Machine) to play Laplace’s Demon. And thus forgoes the need for uncertainty and uncertainty ascertainment. Which obviously clashes with the issues of poor data, inappropriate models, and time or space stationarity of the available information.

Two more computing-related notions that came out the discussion [for me] are asynchronicity (in the sense explored by Terenin et al. a few months ago) and subsampling, The later seems to mean many things, judging from the discussion from the panel and the audience. For me, it corresponded to the ability (or inability) to handle only part of the available data to simulate the posterior associated with this available data.

The first talk on Tuesday morning was the plenary talk by Michael Jordan about his incorporation of complexity constraints on the convergence of an MCMC variable selection algorithm. (I though I had commented this paper in the past on the ‘Og but apparently I did not!) This was quite interesting, with ultra-fast convergence of the sampler. The talk was alas made harder to follow because of a cameraman standing in front of most of the audience for the entire time, as in the above picture. (I also noticed the interesting randomness of the light panels, who all display different patterns of dots, maybe random enough to satisfy a randomness test!) Another if irrelevant annoying fact was that I discovered upon arrival that my airbnb rental was located 8 kilometres away from the conference location, in a completely different town! Thankfully, we had rented a car [for 5] which saved the day (and even more the night!).