One month after I visited UCL-Gatsby and Warwick University, I am back this week in England for short visits to Bristol, Warwick, and Oxford. Presumably not with the same wonderful weather I enjoyed in Bristol during the Fall workshop.
Archive for University of Oxford
back to England
Posted in pictures, Travel, University life with tags Bristol, Clifton suspension bridge, England, University of Oxford, Warwick on March 3, 2013 by xi'anI like…intractable likelihoods (openings)
Posted in Statistics with tags ABC, EPSRC, i-like, intractable likelihood, Lancaster University, likelihood-free methods, openings, University of Bristol, University of Oxford, University of Warwick on December 22, 2012 by xi'an
A new EPSRC programme grant, called i-like, has been awarded to researchers in Bristol, Lancaster, Oxford, and Warwick, to conduct research on intractable likelihoods. (I am also associated to this program as a [grateful] collaborator.) This covers several areas of statistics, like big data and inference on stochastic process, but my own primary interest in the programme is of course the possibilities to conduct collaboration on ABC and composite likelihood methods. (Great website design, by the way!)
A first announcement is that there will be a half-day launch in Oxford on January 31, 2013, which program is now available. Followed by a workshop in mid-May in Warwick (to which I will participate). This event is particularly aimed at PhD students and early-career researchers. The second announcement is that the EPSRC programme grant provides funding for five postdoctoral positions over a duration of four years, which is of course stupendous! So if you like i-like as much as I like it, and are a new researcher looking for opportunities in exciting areas, you should definitely consider applying!
AMOR at 5000ft in a water tank…
Posted in Mountains, pictures, Statistics, University life with tags adaptive MCMC, AMOR, Argentina, clustering, cosmic rays, label switching experiment, LAL, mixture estimation, mixtures, Oxford, permutations, Pierre-Augier experiment, relabelling, Université Paris-Sud, University of Oxford on November 22, 2012 by xi'an
On Monday, I attended the thesis defence of Rémi Bardenet in Orsay as a member (referee) of his thesis committee. While this was a thesis in computer science, which took place in the Linear Accelerator Lab in Orsay, it was clearly rooted in computational statistics, hence justifying my presence in the committee. The justification (!) for the splashy headline of this post is that Rémi’s work was motivated by the Pierre-Auger experiment on ultra-high-energy cosmic rays, where particles are detected through a network of 1600 water tanks spread over the Argentinian Pampa Amarilla on an area the size of Rhode Island (where I am incidentally going next week).
The part of Rémi’s thesis presented during the defence concentrated on his AMOR algorithm, arXived in a paper written with Olivier Cappé and Gersende Fort. AMOR stands for adaptive Metropolis online relabelling and combines adaptive MCMC techniques with relabelling strategies to fight label-switching (e.g., in mixtures). I have been interested in mixtures for eons (starting in 1987 in Ottawa with applying Titterington, Smith, and Makov to chest radiographs) and in label switching for ages (starting at the COMPSTAT conférence in Bristol in 1998). Rémi’s approach to the label switching problem follows the relabelling path, namely a projection of the original parameter space into a smaller subspace (that is also a quotient space) to avoid permutation invariance and lack of identifiability. (In the survey I wrote with Kate Lee, Jean-Michel Marin and Kerrie Mengersen, we suggest using the mode as a pivot to determine which permutation to use on the components of the mixture.) The paper suggests using an Euclidean distance to a mean determined adaptively, μt, with a quadratic form Σt also determined on-the-go, minimising (Pθ-μt)TΣt(Pθ-μt) over all permutations P at each step of the algorithm. The intuition behind the method is that the posterior over the restricted space should look like a roughly elliptically symmetric distribution, or at least like a unimodal distribution, rather than borrowing bits and pieces from different modes. While I appreciate the technical tour de force represented by the proof of convergence of the AMOR algorithm, I remain somehow sceptical about the approach and voiced the following objections during the defence: first, the assumption that the posterior becomes unimodal under an appropriate restriction is not necessarily realistic. Secondary modes often pop in with real data (as in the counter-example we used in our paper with Alessandra Iacobucci and Jean-Michel Marin). Next, the whole apparatus of fighting multiple modes and non-identifiability, i.e. fighting label switching, is to fall back on posterior means as Bayes estimators. As stressed in our JASA paper with Gilles Celeux and Merrilee Hurn, there is no reason for doing so and there are several reasons for not doing so:
- it breaks down under model specification, i.e., when the number of components is not correct
- it does not improve the speed of convergence but, on the opposite, restricts the space visited by the Markov chain
- it may fall victim to the fatal attraction of secondary modes by fitting too small an ellipse around one of those modes
- it ultimately depends on the parameterisation of the model
- there is no reason for using posterior means in mixture problems, posterior modes or cluster centres can be used instead
I am therefore very much more in favour of producing a posterior distribution that is as label switching as possible (since the true posterior is completely symmetric in this respect). Post-processing the resulting sample can be done by using off-the-shelf clustering in the component space, derived from the point process representation used by Matthew Stephens in his thesis and subsequent papers. It also allows for a direct estimation of the number of components.
In any case, this was a defence worth-attending that led me to think afresh about the label switching problem, with directions worth exploring next month while Kate Lee is visiting from Auckland. Rémi Bardenet is now headed for a postdoc in Oxford, a perfect location to discuss further label switching and to engage into new computational statistics research!
English trip (1)
Posted in Statistics, Travel, University life with tags ABC, Cambridge University, CRiSM, Edinburgh, GPU, graphics processing units, Kenilworth, model choice, parallel processing, Scotland, University of Oxford, University of Warwick, Wang-Landau algorithm, Warwick on January 25, 2012 by xi'an
Today, I am attending a workshop on the use of graphics processing units in Statistics in Warwick, supported by CRiSM, presenting our recent works with Randal Douc, Pierre Jacob and Murray Smith. (I will use the same slides as in Telecom two months ago, hopefully avoiding the loss of integral and summation signs this time!) Pierre Jacob will talk about Wang-Landau.
Then, tomorrow, I am off to Cambridge to talk about ABC and model choice on Friday afternoon. (Presumably using the same slides as in Provo.)
The (1) in the title is in prevision of a second trip to Oxford next month and another one to Bristol two months after! (The trip to Edinburgh does not count of course, since it is in Scotland!)
Bayesian tweet
Posted in University life with tags ABC, Bayes 250, Bayesian statistics, BBC, Edinburgh, Thomas Bayes, tweets, University of Oxford on November 14, 2011 by xi'an
After Scott Sisson’s tweet on ABC, Richard Everitt from Oxford (England) has started a Bayesian tweet on all things Bayesian. Which made me realise Bayes 250 had made it to the BBC website!
