a pile of new books

Posted in Books, Travel, University life with tags , , , , , , , , , on November 22, 2014 by xi'an

IMG_2663I took the opportunity of my weekend trip to Gainesville to order a pile of books on amazon, thanks to my amazon associate account (and hence thanks to all Og’s readers doubling as amazon customers!). The picture above is missing two  Rivers of London volumes by Ben Aaraonovitch that I already read and left at the office. And reviewed in incoming posts. Among those,

(Obviously, all “locals” sharing my taste in books are welcome to borrow those in a very near future!)

some LaTeX tricks

Posted in Books, Kids, Statistics, University life with tags , , , , on November 21, 2014 by xi'an

Here are a few LaTeX tricks I learned or rediscovered when working on several papers the past week:

  1. I am always forgetting how to make aligned equations with a single equation number, so I found this solution on the TeX forum of stackexchange, Namely use the equation environment and then an aligned environment inside. Or the split environment. But it does not always work…
  2. Another frustrating black hole is how to deal with integral signs that do not adapt to the integrand. Too bad we cannot use \left\int, really! Another stackexchange question led me to the bigints package. Not perfect though.
  3. Pierre Pudlo also showed me the commands \graphicspath{{dir1}{dir2}} and \DeclareGraphicsExtensions{.pdf,.png,.jpg} to avoid coding the entire path to each image and to put an order on the extension type, respectively. The second one is fairly handy when working on drafts. The first one does not seem to work with symbolic links, though…

not converging to London for an [extra]ordinary Read Paper

Posted in Books, Kids, pictures, Statistics, Travel, University life with tags , , , , , , , on November 21, 2014 by xi'an

London by Delta, Dec. 14, 2011On December 10, I will alas not travel to London to attend the Read Paper on sequential quasi-Monte Carlo presented by Mathieu Gerber and Nicolas Chopin to The Society, as I fly instead to Montréal for the NIPS workshops… I am quite sorry to miss this event, as this is a major paper which brings quasi-Monte Carlo methods into mainstream statistics. I will most certainly write a discussion and remind Og’s readers that contributed (800 words) discussions are welcome from everyone, the deadline for submission being January 02.

limbo IPA

Posted in pictures, Travel, Wines with tags , , , , , , , on November 20, 2014 by xi'an


Bayesian evidence and model selection

Posted in Statistics on November 20, 2014 by xi'an

Another arXived paper with a topic close to my interests, posted by Knuth et al. today. Namely, Bayesian model selection. However, after reading the paper in Gainesville, I am rather uncertain about its prospects, besides providing an entry to the issue (for physicists?). Indeed, the description of (Bayesian) evidence is concentrating on rough approximations, in a physics perspective, with a notion of Occam’s factor that measures the divergence to the maximum likelihood. (As usual when reading physics literature, I am uncertain as to why one should consider always approximations.) The numerical part mentions the tools of importance sampling and Laplace approximations, path sampling and nested sampling. The main part of the paper consists in applying those tools to signal processing models. One of them is a mixture example where nested sampling is used to evaluate the most likely number of components. Using uniform priors over non-specified hypercubes. In an example about photometric signal from an exoplanet, two models are distinguished by evidences of 37,764 and 37,765, with another one at 37,748. It seems to me that this very proximity simply prevents the comparison of those models, even without accounting for the Monte Carlo variability. And does not suffice to conclude about a scientific theory (“effectively characterize exoplanetary systems”). Which leads to my current thinking, already expressed on that blog, that Bayes factors and posterior probabilities should be replaced with an alternative, including uncertainty about the very Bayes factor (or evidence).

differences between Bayes factors and normalised maximum likelihood

Posted in Books, Kids, Statistics, University life with tags , , , , on November 19, 2014 by xi'an

A recent arXival by Heck, Wagenmaker and Morey attracted my attention: Three Qualitative Differences Between Bayes Factors and Normalized Maximum Likelihood, as it provides an analysis of the differences between Bayesian analysis and Rissanen’s Optimal Estimation of Parameters that I reviewed a while ago. As detailed in this review, I had difficulties with considering the normalised likelihood

p(x|\hat\theta_x) \big/ \int_\mathcal{X} p(y|\hat\theta_y)\,\text{d}y

as the relevant quantity. One reason being that the distribution does not make experimental sense: for instance, how can one simulate from this distribution? [I mean, when considering only the original distribution.] Working with the simple binomial B(n,θ) model, the authors show the quantity corresponding to the posterior probability may be constant for most of the data values, produces a different upper bound and hence a different penalty of model complexity, and may differ in conclusion for some observations. Which means that the apparent proximity to using a Jeffreys prior and Rissanen’s alternative does not go all the way. While it is a short note and only focussed on producing an illustration in the Binomial case, I find it interesting that researchers investigate the Bayesian nature (vs. artifice!) of this approach…

importance sampling schemes for evidence approximation [revised]

Posted in Statistics, University life with tags , , , , , , , on November 18, 2014 by xi'an

After a rather intense period of new simulations and versions, Juong Een (Kate) Lee and I have now resubmitted our paper on (some) importance sampling schemes for evidence approximation in mixture models to Bayesian Analysis. There is no fundamental change in the new version but rather a more detailed description of what those importance schemes mean in practice. The original idea in the paper is to improve upon the Rao-Blackwellisation solution proposed by Berkoff et al. (2002) and later by Marin et al. (2005) to avoid the impact of label switching on Chib’s formula. The Rao-Blackwellisation consists in averaging over all permutations of the labels while the improvement relies on the elimination of useless permutations, namely those that produce a negligible conditional density in Chib’s (candidate’s) formula. While the improvement implies truncated the overall sum and hence induces a potential bias (which was the concern of one referee), the determination of the irrelevant permutations after relabelling next to a single mode does not appear to cause any bias, while reducing the computational overload. Referees also made us aware of many recent proposals that conduct to different evidence approximations, albeit not directly related with our purpose. (One was Rodrigues and Walker, 2014, discussed and commented in a recent post.)


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