Archive for the Kids Category

the forever war [book review]

Posted in Books, Kids with tags , , , , , on April 26, 2015 by xi'an

Another book I bought somewhat on a whim, although I cannot remember which one… The latest edition has a preface by John Scalzi, author of Old Man’s War and its sequels, where he acknowledged he would not have written this series, had he previously read The Forever War. Which strikes me as ironical as I found Scalzi’s novels way better. Deeper. And obviously not getting obsolete so immediately! (As an aside, Scalzi is returning to the Old Man’s War universe with a new novel, The End of All Things.)

“…it’s easy to compute your chances of being able to fight it out for ten years. It comes to about two one-thousandths of one percent. Or, to put it another way, get an old-fashioned six-shooter and play Russian Roulette with four of the six chambers loaded. If you can do it ten times in a row without decorating the opposite wall, congratulations! You’re a civilian.”

This may be the main issue with The Forever War. The fact that it sounds so antiquated. And hence makes reading the novel like an exercise in Creative Writing 101, in order to spot how the author was so rooted in the 1970’s that he could not project far enough in the future to make his novel sustainable. The main issue in the suspension of belief required to proceed through the book is the low-tech configuration of Halderman’s future. Even though intergalactic travel is possible via the traditional portals found in almost every sci’-fi’ book, computers are blatantly missing from the picture. And so is artificial intelligence as well. (2001 A space odyssey was made in 1968, right?!) The economics of a forever warring Earth are quite vague and unconvincing. There is no clever tactics in the war against the Taurans. Even the battle scenes are far from exciting. Esp. the parts where they fight with swords and arrows. And the treatment of sexuality has not aged well. So all that remains in favour of the story (and presumably made the success of the book) is the description of the ground soldier’s life which could almost transcribe verbatim to another war and another era. End of the story. (Unsurprisingly, while being the first book picked for the SF MasterworksThe Forever War did not make it into the 2011 series…)

ontological argument

Posted in Books, Kids, pictures with tags , , on April 25, 2015 by xi'an

simulating correlated Binomials [another Bernoulli factory]

Posted in Books, Kids, pictures, R, Running, Statistics, University life with tags , , , , , , , on April 21, 2015 by xi'an

This early morning, just before going out for my daily run around The Parc, I checked X validated for new questions and came upon that one. Namely, how to simulate X a Bin(8,2/3) variate and Y a Bin(18,2/3) such that corr(X,Y)=0.5. (No reason or motivation provided for this constraint.) And I thought the following (presumably well-known) resolution, namely to break the two binomials as sums of 8 and 18 Bernoulli variates, respectively, and to use some of those Bernoulli variates as being common to both sums. For this specific set of values (8,18,0.5), since 8×18=12², the solution is 0.5×12=6 common variates. (The probability of success does not matter.) While running, I first thought this was a very artificial problem because of this occurrence of 8×18 being a perfect square, 12², and cor(X,Y)x12 an integer. A wee bit later I realised that all positive values of cor(X,Y) could be achieved by randomisation, i.e., by deciding the identity of a Bernoulli variate in X with a Bernoulli variate in Y with a certain probability ϖ. For negative correlations, one can use the (U,1-U) trick, namely to write both Bernoulli variates as

X_1=\mathbb{I}(U\le p)\quad Y_1=\mathbb{I}(U\ge 1-p)

in order to minimise the probability they coincide.

I also checked this result with an R simulation

> z=rbinom(10^8,6,.66)
> y=z+rbinom(10^8,12,.66)
> x=z+rbinom(10^8,2,.66)
cor(x,y)
> cor(x,y)
[1] 0.5000539

Searching on Google gave me immediately a link to Stack Overflow with an earlier solution with the same idea. And a smarter R code.

Bayesian propaganda?

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , on April 20, 2015 by xi'an

“The question is about frequentist approach. Bayesian is admissable [sic] only by wrong definition as it starts with the assumption that the prior is the correct pre-information. James-Stein beats OLS without assumptions. If there is an admissable [sic] frequentist estimator then it will correspond to a true objective prior.”

I had a wee bit of a (minor, very minor!) communication problem on X validated, about a question on the existence of admissible estimators of the linear regression coefficient in multiple dimensions, under squared error loss. When I first replied that all Bayes estimators with finite risk were de facto admissible, I got the above reply, which clearly misses the point, and as I had edited the OP question to include more tags, the edited version was reverted with a comment about Bayesian propaganda! This is rather funny, if not hilarious, as (a) Bayes estimators are indeed admissible in the classical or frequentist sense—I actually fail to see a definition of admissibility in the Bayesian sense—and (b) the complete class theorems of Wald, Stein, and others (like Jack Kiefer, Larry Brown, and Jim Berger) come from the frequentist quest for best estimator(s). To make my point clearer, I also reproduced in my answer the Stein’s necessary and sufficient condition for admissibility from my book but it did not help, as the theorem was “too complex for [the OP] to understand”, which shows in fine the point of reading textbooks!

the luminaries [book review]

Posted in Books, Kids, Mountains, Travel with tags , , , , , , , , on April 18, 2015 by xi'an

I bought this book by Eleanor Catton on my trip to Pittsburgh and Toronto in 2013 (thanks to Amazon associates’ gains!), mostly by chance (and also because it was the most recent Man Booker Prize). After a few sleepless nights last week (when I should not have been suffering from New York jet lag!, given my sleeping pattern when abroad), I went through this rather intellectual and somewhat contrived mystery. To keep with tradition (!), the cover was puzzling me until I realised those were phases of the moon, in line with [spoiler!] the zodiacal underlying pattern of the novel, pattern I did not even try to follow for it sounded so artificial. And presumably restricted the flow of the story by imposing further constraints on the characters’ interactions.

The novel has redeeming features, even though I am rather bemused at it getting a Man Booker Prize. (When compared with, say, The Remains of the Day…) For one thing, while a gold rush story of the 1860’s, it takes place on the South Island of New Zealand instead of Klondike, around the Hokitika gold-field, on the West Coast, with mentions of places that brings memory of our summer (well, winter!) visit to Christchurch in 2006… The mix of cultures between English settlers, Maoris, and Chinese migrants, is well-documented and information, if rather heavy at times, bordering on the info-dump, and a central character like the Maori Te Rau Tauwhare sounds caricaturesque. The fact that the story takes place in Victorian times call Dickens to mind, but I find very little connection in either style or structure, nor with Victorian contemporaries like Wilkie Collins, and Victorian pastiches like Charles Palliser‘s Quincunx…. Nothing of the sanctimonious and moral elevation and subtle irony one could expect from a Victorian novel!

While a murder mystery, the plot is fairly upside down (or down under?!): the (spoiler!) assumed victim is missing for most of the novel, the (spoiler!) extracted gold is not apparently stolen but rather lacks owner(s), and the most moral character of the story ends up being the local prostitute. The central notion of the twelve men in a council each bringing a new light on the disappearance of Emery Staines is a neat if not that innovative literary trick but twelve is a large number which means following many threads, some being dead-ends, to gather an appearance of a view on the whole story. As in Rashomon, one finishes the story with a deep misgiving as to who did what, after so many incomplete and biased accountings. Unlike Rashomon, it alas takes forever to reach this point!

ah ces enseignants..!

Posted in Kids, pictures, Travel on April 16, 2015 by xi'an

belgie

reis naar Amsterdam

Posted in Books, Kids, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , on April 16, 2015 by xi'an

Amster4On Monday, I went to Amsterdam to give a seminar at the University of Amsterdam, in the department of psychology. And to visit Eric-Jan Wagenmakers and his group there. And I had a fantastic time! I talked about our mixture proposal for Bayesian testing and model choice without getting hostile or adverse reactions from the audience, quite the opposite as we later discussed this new notion for several hours in the café across the street. I also had the opportunity to meet with Peter Grünwald [who authored a book on the minimum description length principle] pointed out a minor inconsistency of the common parameter approach, namely that the Jeffreys prior on the first model did not have to coincide with the Jeffreys prior on the second model. (The Jeffreys prior for the mixture being unavailable.) He also wondered about a more conservative property of the approach, compared with the Bayes factor, in the sense that the non-null parameter could get closer to the null-parameter while still being identifiable.

Amster6Among the many persons I met in the department, Maarten Marsman talked to me about his thesis research, Plausible values in statistical inference, which involved handling the Ising model [a non-sparse Ising model with O(p²) parameters] by an auxiliary representation due to Marc Kac and getting rid of the normalising (partition) constant by the way. (Warning, some approximations involved!) And who showed me a simple probit example of the Gibbs sampler getting stuck as the sample size n grows. Simply because the uniform conditional distribution on the parameter concentrates faster (in 1/n) than the posterior (in 1/√n). This does not come as a complete surprise as data augmentation operates in an n-dimensional space. Hence it requires more time to get around. As a side remark [still worth printing!], Maarten dedicated his thesis as “To my favourite random variables , Siem en Fem, and to my normalizing constant, Esther”, from which I hope you can spot the influence of at least two of my book dedications! As I left Amsterdam on Tuesday, I had time for a enjoyable dinner with E-J’s group, an equally enjoyable early morning run [with perfect skies for sunrise pictures!], and more discussions in the department. Including a presentation of the new (delicious?!) Bayesian software developed there, JASP, which aims at non-specialists [i.e., researchers unable to code in R, BUGS, or, God forbid!, STAN] And about the consequences of mixture testing in some psychological experiments. Once again, a fantastic time discussing Bayesian statistics and their applications, with a group of dedicated and enthusiastic Bayesians!Amster12

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