Archive for the University life Category

big data, big models, it is a big deal!

Posted in pictures, Statistics, University life with tags , , , , , on September 2, 2014 by xi'an

bdbmbd

efficient exploration of multi-modal posterior distributions

Posted in Books, Statistics, University life with tags , , , , on September 1, 2014 by xi'an

The title of this recent arXival had potential appeal, however the proposal ends up being rather straightforward and hence  anti-climactic! The paper by Hu, Hendry and Heng proposes to run a mixture of proposals centred at the various modes of  the target for an efficient exploration. This is a correct MCMC algorithm, granted!, but the requirement to know beforehand all the modes to be explored is self-defeating, since the major issue with MCMC is about modes that are  omitted from the exploration and remain undetected throughout the simulation… As provided, this is a standard MCMC algorithm with no adaptive feature and I would rather suggest our population Monte Carlo version, given the available information. Another connection with population Monte Carlo is that I think the performances would improve by Rao-Blackwellising the acceptance rate, i.e. removing the conditioning on the (ancillary) component of the index. For PMC we proved that using the mixture proposal in the ratio led to an ideally minimal variance estimate and I do not see why randomising the acceptance ratio in the current case would bring any improvement.

high-dimensional stochastic simulation and optimisation in image processing [day #3]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on August 31, 2014 by xi'an

Last and maybe most exciting day of the “High-dimensional Stochastic Simulation and Optimisation in Image Processing” in Bristol as it was exclusively about simulation (MCMC) methods. Except my own talk on ABC. And Peter Green’s on consistency of Bayesian inference in non-regular models. The talks today were indeed about using convex optimisation devices to speed up MCMC algorithms with tools that were entirely new to me, like the Moreau transform discussed by Marcelo Pereyra. Or using auxiliary variables à la RJMCMC to bypass expensive Choleski decompositions. Or optimisation steps from one dual space to the original space for the same reason. Or using pseudo-gradients on partly differentiable functions in the talk by Sylvain Lecorff on a paper commented earlier in the ‘Og. I particularly liked the notion of Moreau regularisation that leads to more efficient Langevin algorithms when the target is not regular enough. Actually, the discretised diffusion itself may be geometrically ergodic without the corrective step of the Metropolis-Hastings acceptance. This obviously begs the question of an extension to Hamiltonian Monte Carlo. And to multimodal targets, possibly requiring as many normalisation factors as there are modes. So, in fine, a highly informative workshop, with the perfect size and the perfect crowd (which happened to be predominantly French, albeit from a community I did not have the opportunity to practice previously). Massive kudos to Marcello for putting this workshop together, esp. on a week where family major happy events should have kept him at home!

As the workshop ended up in mid-afternoon, I had plenty of time for a long run with Florence Forbes down to the Avon river and back up among the deers of Ashton Court, avoiding most of the rain, all of the mountain bikes on a bike trail that sounded like trail running practice, and building enough of an appetite for the South Indian cooking of the nearby Thali Café. Brilliant!

high-dimensional stochastic simulation and optimisation in image processing [day #2]

Posted in pictures, Statistics, Travel, Uncategorized, University life, Wines with tags , , , , , , on August 30, 2014 by xi'an

After a nice morning run down Leigh Woods and on the muddy banks of the Avon river, I attended a morning session on hyperspectral image non-linear modelling. Topic about which I knew nothing beforehand. Hyperspectral images are 3-D images made of several wavelengths to improve their classification as a mixture of several elements. The non-linearity is due to the multiple reflections from the ground as well as imperfections in the data collection. I found this new setting of clear interest, from using mixtures to exploring Gaussian processes and Hamiltonian Monte Carlo techniques on constrained spaces… Not to mention the “debate” about using Bayesian inference versus optimisation. It was overall a day of discovery as I am unaware of the image processing community (being the outlier in this workshop!) and of their techniques. The problems mostly qualify as partly linear high-dimension inverse problems, with rather standard if sometimes hybrid MCMC solutions. (The day ended even more nicely with another long run in the fields of Ashton Court and a conference diner by the river…)

 

high-dimensional stochastic simulation and optimisation in image processing [day #1]

Posted in pictures, Statistics, Travel, Uncategorized, University life, Wines with tags , , , , , , , , , , , on August 29, 2014 by xi'an

Even though I flew through Birmingham (and had to endure the fundamental randomness of trains in Britain), I managed to reach the “High-dimensional Stochastic Simulation and Optimisation in Image Processing” conference location (in Goldney Hall Orangery) in due time to attend the (second) talk by Christophe Andrieu. He started with an explanation of the notion of controlled Markov chain, which reminded me of our early and famous-if-unpublished paper on controlled MCMC. (The label “controlled” was inspired by Peter Green who pointed out to us the different meanings of controlled in French [meaning checked or monitored] and in English . We use it here in the English sense, obviously.) The main focus of the talk was on the stability of controlled Markov chains. With of course connections with out controlled MCMC of old, for instance the case of the coerced acceptance probability. Which happened to be not that stable! With the central tool being Lyapounov functions. (Making me wonder whether or not it would make sense to envision the meta-problem of adaptively estimating the adequate Lyapounov function from the MCMC outcome.)

As I had difficulties following the details of the convex optimisation talks in the afternoon, I eloped to work on my own and returned to the posters & wine session, where the small number of posters allowed for the proper amount of interaction with the speakers! Talking about the relevance of variational Bayes approximations and of possible tools to assess it, about the use of new metrics for MALA and of possible extensions to Hamiltonian Monte Carlo, about Bayesian modellings of fMRI and of possible applications of ABC in this framework. (No memorable wine to make the ‘Og!) Then a quick if reasonably hot curry and it was already bed-time after a rather long and well-filled day!z

capture-recapture homeless deaths

Posted in Statistics, Travel, University life with tags , , , , , , , on August 28, 2014 by xi'an

Paris and la Seine, from Pont du Garigliano, Oct. 20, 2011In the newspaper I grabbed in the corridor to my plane today (flying to Bristol to attend the SuSTaIn image processing workshop on “High-dimensional Stochastic Simulation and Optimisation in Image Processing” where I was kindly invited and most readily accepted the invitation), I found a two-page entry on estimating the number of homeless deaths using capture-recapture. Besides the sheer concern about the very high mortality rate among homeless persons (expected lifetime, 48 years; around 7000 deaths in France between 2008 and 2010) and the dreadful realisation that there are an increasing number of kids dying in the streets, I was obviously interested in this use of capture-recapture methods as I had briefly interacted with researchers from INED working on estimating the number of (living) homeless persons about 15 years ago. Glancing at the original paper once I had landed, there was alas no methodological innovation in the approach, which was based on the simplest maximum likelihood estimate. I wonder whether or not more advanced models and [Bayesian] methods of inference could [or should] be used on such data. Like introducing covariates in the process. For instance, when conditioning the probability of (cross-)detection on the cause of death.

NIPS workshops (Dec. 12-13, 2014, Montréal)

Posted in Kids, Statistics, Travel, University life with tags , , , , , , , , on August 25, 2014 by xi'an

Run_ABCFollowing a proposal put forward by Ted Meeds, Max Welling,  Richard Wilkinson, Neil Lawrence and myself, our ABC in Montréal workshop has been accepted by the NIPS 2014 committee and will thus take place on either Friday, Dec. 11, or Saturday, Dec. 12, at the end of the main NIPS meeting (Dec. 8-10). (Despite the title, this workshop is not part of the ABC in … series I started five years ago. It will only last a single day with a few invited talks and no poster. And no free wine & cheese party.) On top of this workshop, our colleagues Vikash K Mansinghka, Daniel M Roy, Josh Tenenbaum, Thomas Dietterich, and Stuart J Russell have also been successful in their bid for the 3rd NIPS Workshop on Probabilistic Programming which will presumably be held on the opposite day to ours, as Vikash is speaking at our workshop, while I am speaking in this workshop. I am yet undecided as to whether or not to attend the main conference, given that I am already travelling a lot this semester and have to teach two courses, incl. a large undergraduate statistics inference course… Obviously, I will try to attend if our joint paper is accepted by the editorial board! Even though Marco will then be the speaker.

Follow

Get every new post delivered to your Inbox.

Join 634 other followers