Archive for objective Bayes

Advances in scalable Bayesian computation [day #1]

Posted in Books, Mountains, pictures, R, Statistics, University life with tags , , , , , , , , , on March 4, 2014 by xi'an

polyptych painting within the TransCanada Pipeline Pavilion, Banff Centre, Banff, March 21, 2012This was the first day of our workshop Advances in Scalable Bayesian Computation and it sounded like the “main” theme was probabilistic programming, in tune with my book review posted this morning. Indeed, both Vikash Mansinghka and Frank Wood gave talks about this concept, Vikash detailing the specifics of a new programming language called Venture and Frank focussing on his state-space version of the above called Anglican. This is a version of the language Church, developed to handle probabilistic models and inference (hence the joke about Anglican, “a Church of England Venture’! But they could have also added that Frank Wood was also the name of a former archbishop of Melbourne..!) I alas had an involuntary doze during Vikash’s talk, which made it harder for me to assess the fundamentals of those ventures, of how they extended beyond a “mere” new software (and of why I would invest in learning a Lisp-based language!).

The other talks of Day #1 were of a more “classical” nature with Pierre Jacob explaining why non-negative unbiased estimators were impossible to provide in general, a paper I posted about a little while ago, and including an objective Bayes example that I found quite interesting. Then Sumeet Singh (no video) presented a joint work with Nicolas Chopin on the uniform ergodicity of the particle Gibbs sampler, a paper that I should have commented here (except that it appeared just prior to The Accident!), with a nice coupling proof. And Maria Lomeli gave us an introduction to the highly general Poisson-Kingman mixture models as random measures, which encompasses all of the previously studied non-parametric random measures, with an MCMC implementation that included a latent variable representation for the alpha-stable process behind the scene, representation that could be (and maybe is) also useful in parametric analyses of alpha-stable processes.

We also had an open discussion in the afternoon that ended up being quite exciting, with a few of us voicing out some problems or questions about existing methods and others making suggestions or contradictions. We are still a wee bit short of considering a collective paper on MCMC under constraints with coherent cross-validated variational Bayes and loss-based pseudo priors, with applications to basketball data” to appear by the end of the week!

Add to this two visits to the Sally Borden Recreation Centre for morning swimming and evening climbing, and it is no wonder I woke up a bit late this morning! Looking forward Day #2!

on alternative perspectives and solutions on Bayesian tests

Posted in Statistics, Travel, University life with tags , , , , , , , on December 16, 2013 by xi'an

Here are the slides of my tutorial at O’ Bayes 2013 today, a pot-pourri of various, recent and less recent, criticisms (with, albeit less than usual, a certain proportion of recycled slides):

off to Duke

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , on December 15, 2013 by xi'an

IMG_2181On my way to Duke and O’Bayes 2013, I took an early flight to Atlanta, with a bit of a delay because of a faulty tractor in Charles de Gaulle airport but all in all an overall smooth trip. We alas flew too much south this time to get any view of Greenland except for the glimpse below… Apart from working on my slides for today’s lecture, I watched bits (actually most) of rather silly films, The Lone Ranger and Oblivion, not really worth reviewing here. (The former is playing too much on second degree references to Pirates of the Caribbean. From Johnny Depp making faces to his playing with his watch, to the recurrent madman wearing women’s clothes and playing with an umbrella. The second one was just appalling, from the abysmally poor acting to the ultimate absence of a plot…) I also read in The NYT about a new super-prize in Mathematics to be launched by a few “philanthropists”, including Mark Zuckerberg. The paper was not giving any detail on the focus of the prize and on the motives of the generous donators. Interestingly, the similar prize they set for physics went to two proponents of string theory, which is still a mathematical construct with no experimental evidence, as far as I understand…IMG_2176Rereading Johnson’s PNAS paper for my tutorial had the side result of making me realise him using a flat prior on a normal mean without more justification than there is a “constant factor that arises from the uniform distribution on μ”…

a talk with Jay

Posted in Books, Running, Statistics, Travel, University life with tags , , , , , , , , , , on November 1, 2013 by xi'an

IMG_1900I had a wonderful time in CMU, talking with a lot of faculty about their research (and mine), like reminiscing of things past and expanding on things to come with Larry (not to mention exchanging blogging impressions), giving my seminar talk, having a great risotto at Casbah, and a nice dinner at Legume, going for morning runs in the nearby park… One particularly memorable moment was the discussion I had with Jay as/since he went back to our diverging views about objective Bayes and improper priors, as expressed in the last chapter of his book and my review of it. While we kept disagreeing on their relevance and on whether or not they should be used, I had to concede that one primary reason for using reference priors is one of laziness in not seeking expert opinions. Even though there always is a limit to the information provided by such experts that means a default input at one level or the next (of a hierarchical model). Jay also told me of his proposal (as reported in his 1996 Bayesian methods and ethics in a clinical trial design book) for conducting clinical trials with several experts (with different priors) and sequentially weighting them by their predictive success. Proposal which made me think of a sequential way to compare models by their predictive abilities and still use improper priors…


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