Archive for Harvard Square

principles or unprincipled?!

Posted in Books, Kids, pictures, Statistics, Travel with tags , , , , , , , on May 2, 2017 by xi'an

A lively and wide-ranging discussion during the Bayes, Fiducial, Frequentist conference was about whether or not we should look for principles. Someone mentioned Terry Speed (2016) claim that it does not help statistics in being principled. Against being efficient. Which gets quite close in my opinion to arguing in favour of a no-U-turn move to machine learning—which requires a significant amount of data to reach this efficiency, as Xiao-Li Meng mentioned—. The debate brought me back to my current running or droning argument on the need to accommodate [more] the difference between models and reality. Not throwing away statistics and models altogether, but developing assessments that are not fully chained to those models. While keeping probabilistic models to handle uncertainty. One pessimistic conclusion I drew from the discussion is that while we [as academic statisticians] may set principles and even teach our students how to run principled and ethical statistical analyses, there is not much we can do about the daily practice of users of statistics…

JSM 2014, Boston [#2]

Posted in Statistics, Travel, University life with tags , , , , , , , , on August 7, 2014 by xi'an

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.)