L’Armée Furieuse [book review]

Posted in Books, Travel with tags , , , , , , on December 9, 2018 by xi'an

“On dit que les Normands n’aiment pas beaucoup parler… Ce n’est pas qu’ils n’aiment pas parler, c’est qu’ils n’aiment pas répondre. Ce n’est pas la même chose.”

I picked this book by Fred Vargas at the airport mostly because the back cover mentioned Orbec a town near my hometown in rural Normandy. With a slight misspelling to avoid legal issues I presume. It made for a nice read in the long trip to Oaxaca even though it is filled with impossibilities and incoherences. The crux of the story is an interesting medieval myth called l’armée furieuse (the Wild Hunt) that tells of a spectral army crossing the North of France and picking dammed souls soon to die. The wild hunt is also called la mesnie or maisnie Hellequin, from the name of the Lord leading the spectral army. According to a English monk from a Norman monastery in the 1100’s. Myth that some in current era want to exploit to cover real crimes. As in the previous novels of Fred Vargas that I read there is an interesting undercurrent of exposing the machinery of a rural community, with highly unorthodox police officers. Not that I recognized much of my hometown atmosphere. And the Deus ex Machina represented by a local count [historically speaking, Orbec is only a barony] and the industrial plot were by far too implausible! (With a geographical inaccuracy of setting La Touques river nearby. And of mentioning a train station in Cernay, to end up on a very picky note.)

hue & cry [book review]

Posted in Statistics with tags , , , , , , on December 8, 2018 by xi'an

While visiting the Blackwell’s bookstore by the University of Edinburgh last June, I spotted this historical whodunit in the local interest section. Hue & Cry by Shirley McKay. It stayed on a to-read pile by my bed until a few weeks ago when I started reading it and got more and more engrossed in the story. While the style is not always at its best and the crime aspects are somewhat thin, I find the description of the Scottish society of the time (1570’s) fascinating (and hopefully accurate), especially the absolute dominion of the local Church (Kirk) on every aspect of life and the helplessness of women always under the threat of witchcraft accusations. Which could end up with the death penalty, as in thousands of cases. The book reminds me to some extent of the early Susanna Gregory’s books in that it also involves scholars, teaching well-off students with limited intellectual abilities, while bright but poorer students have to work for the college to make up for their lack of funds. As indicated above, the criminal part is less interesting as the main investigator unfolds the complicated plot without much of a hint. And convinces the juries rather too easily in my opinion. An overall fine novel, nonetheless!

polluters 3 [taxes] – government 0 [result] – climate minus 1 [or rather +2⁰]

Posted in pictures with tags , , , , , , , , on December 7, 2018 by xi'an

selected parameters from observations

Posted in Books, Statistics with tags , , , , , , , on December 7, 2018 by xi'an

I recently read a fairly interesting paper by Daniel Yekutieli on a Bayesian perspective for parameters selected after viewing the data, published in Series B in 2012. (Disclaimer: I was not involved in processing this paper!)

The first example is to differentiate the Normal-Normal mean posterior when θ is N(0,1) and x is N(θ,1) from the restricted posterior when θ is N(0,1) and x is N(θ,1) truncated to (0,∞). By restating the later as the repeated generation from the joint until x>0. This does not sound particularly controversial, except for the notion of selecting the parameter after viewing the data. That the posterior support may depend on the data is not that surprising..!

“The observation that selection affects Bayesian inference carries the important implication that in Bayesian analysis of large data sets, for each potential parameter, it is necessary to explicitly specify a selection rule that determines when inference  is provided for the parameter and provide inference that is based on the selection-adjusted posterior distribution of the parameter.” (p.31)

The more interesting distinction is between “fixed” and “random” parameters (Section 2.1), which separate cases where the data is from a truncated distribution (given the parameter) and cases where the joint distribution is truncated but misses the normalising constant (function of θ) for the truncated sampling distribution. The “mixed” case introduces an hyperparameter λ and the normalising constant integrates out θ and depends on λ. Which amounts to switching to another (marginal) prior on θ. This is quite interesting even though one can debate of the very notions of “random” and “mixed” “parameters”, which are those where the posterior most often changes, as true parameters. Take for instance Stephen Senn’s example (p.6) of the mean associated with the largest observation in a Normal mean sample, with distinct means. When accounting for the distribution of the largest variate, this random variable is no longer a Normal variate with a single unknown mean but it instead depends on all the means of the sample. Speaking of the largest observation mean is therefore misleading in that it is neither the mean of the largest observation, nor a parameter per se since the index [of the largest observation] is a random variable induced by the observed sample.

In conclusion, a very original article, if difficult to assess as it can be argued that selection models other than the “random” case result from an intentional modelling choice of the joint distribution.

 

support for Remain comes first in latest YouGov survey

Posted in pictures, Travel with tags , , , , , , , on December 6, 2018 by xi'an

à la maison des mathématiciens [Jean Morlet chair, CIRM, Luminy]

Posted in pictures, Travel, University life with tags , , , , , , , , , , , , , on December 6, 2018 by xi'an

Big Bayes goes South

Posted in Books, Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , on December 5, 2018 by xi'an

At the Big [Data] Bayes conference this week [which I found quite exciting despite a few last minute cancellations by speakers] there were a lot of clustering talks including the ones by Amy Herring (Duke), using a notion of centering that should soon appear on arXiv. By Peter Müller (UT, Austin) towards handling large datasets. Based on a predictive recursion that takes one value at a time, unsurprisingly similar to the update of Dirichlet process mixtures. (Inspired by a 1998 paper by Michael Newton and co-authors.) The recursion doubles in size at each observation, requiring culling of negligible components. Order matters? Links with Malsiner-Walli et al. (2017) mixtures of mixtures. Also talks by Antonio Lijoi and Igor Pruenster (Boconni Milano) on completely random measures that are used in creating clusters. And by Sylvia Frühwirth-Schnatter (WU Wien) on creating clusters for the Austrian labor market of the impact of company closure. And by Gregor Kastner (WU Wien) on multivariate factor stochastic models, with a video of a large covariance matrix evolving over time and catching economic crises. And by David Dunson (Duke) on distance clustering. Reflecting like myself on the definitely ill-defined nature of the [clustering] object. As the sample size increases, spurious clusters appear. (Which reminded me of a disagreement I had had with David McKay at an ICMS conference on mixtures twenty years ago.) Making me realise I missed the recent JASA paper by Miller and Dunson on that perspective.

Some further snapshots (with short comments visible by hovering on the picture) of a very high quality meeting [says one of the organisers!]. Following suggestions from several participants, it would be great to hold another meeting at CIRM in a near future. Continue reading