JSM 2014, Boston [#2]

Day #2 at JSM started quite early as I had to be on site by 7am for the CHANCE editors breakfast. No running then, except to Porter metro station. Interesting exchange full of new ideas to keep the journal cruising. In particular, a call for proposals on special issues on sexy topics (reproducible research anyone? I already have some book reviews.). And directions to increase the international scope and readership. And possibly adding or reporting on a data challenge. After this great start, I attended the Bayesian Time Series and Dynamic Models session, where David Scott Matteson from Cornell University presented an extension of the Toronto ambulance data analysis Dawn Woodard had exposed in Banff at an earlier workshop. The extension dealt with the spatio-temporal nature of the data,  using a mixture model with time-dependent weights that revolved cyclically in an autoexponential manner. And rekindling the interest in the birth-and-death alternative to reversible jump. Plus another talk by Scott Holan mixing Bayesian analysis with frequency data, an issue that always puzzled me. The second session I attended was Multiscale Modeling for Complex Massive Data, with a modelling of brain connections through a non-parametric mixture by David Dunson. And a machine learning talk by Mauro Maggioni on a projection cum optimisation technique to fight the curse of dimension. Who proposed a solution to an optimal transport problem that is much more convincing than the one I discussed a while ago. Unfortunately, this made me miss the Biometrics showcase session, where Debashis Mondal presented a joint work with Julian Besag on Exact Goodness-of-Fit Tests for Markov Chains. And where both my friends Michael Newton and Peter Green were discussants… An idle question that came to me during this last talk was about the existence of particle filters for spatial Markov structures (rather than the usual ones on temporal Markov models).

After a [no] lunch break spent on pondering over a conjecture laid to me by Natesh Pillai yesterday, I eventually joined the Feature Allocation session. Eventually as I basically had to run the entire perimeter of the conference centre! The three talks by Finale Doshi-Velez, Tamara Broderick, and Yuan Ji were all impressive and this may have been my best session so far at JSM! Thanks to Peter Müller for organising it! Tamara Broderick focussed on a generic way to build conjugate priors for non-parametric models, with all talks involving Indian buffets. Maybe a suggestion for tonight’s meal..! (In the end, great local food onn Harvard Square.)

5 Responses to “JSM 2014, Boston [#2]”

  1. Regarding your question on particle filters for spatial models, we have actually recently spent some time on this. Early work can be found in two arXiv papers:

    Sequential Monte Carlo for Graphical Models
    http://arxiv.org/abs/1402.0330v2

    Divide-and-Conquer with Sequential Monte Carlo
    http://arxiv.org/abs/1406.4993

    The first one is basically “standard” particle filters applied in a more general setting using the probabilistic graphical model framework. The second is a completely new SMC algorithm motivated by tree structured graphs but also interesting for spatial patterns.

  2. Dan Simpson Says:

    I could be way off here, but “random field particle filters” seem a bit wasteful. You could probably make one, but the downside would be that ergodicity is still one dimensional, so one dimensional translations (asymptotically) preserve measure, which is lovely, but what are you doing with all the other directions? Maybe you could decrease the variance by averaging over several directions… or are you thinking more that you could build some inter-particle dependence, so rather than having n particles on \Omega x R^+, you have one “point process” on \Omega x R^+, which would allow for inter-point dependence. (There’s a lot of stuff done on Markov point processes and most of the theory holds on abstract spaces, but it’s a bit of a computational disaster…)

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