## Le Monde puzzle [#1062]

Posted in Books, Kids, pictures, R with tags , , , , , on July 28, 2018 by xi'an

A simple Le Monde mathematical puzzle none too geometric:

1. Find square triangles which sides are all integers and which surface is its perimeter.
2. Extend to non-square rectangles.

No visible difficulty by virtue of Pythagore’s formula:

for (a in 1:1e4)
for (b in a:1e4)
if (a*b==2*(a+b+round(sqrt(a*a+b*b)))) print(c(a,b))

produces two answers

 5 12
6  8


and in the more general case, Heron’s formula to the rescue!,

for (a in 1:1e2)
for (b in a:1e2)
for (z in b:1e2){
s=(a+b+z)/2
if (abs(4*s-abs((s-a)*(s-b)*(s-z)))<1e-4) print(c(a,b,z))}

returns

 4 15 21
5  9 16
5 12 13
6  7 15
6  8 10
6 25 29
7 15 20
9 10 17


## the worst possible proof [X’ed]

Posted in Books, Kids, Statistics, University life with tags , , , , , , on July 18, 2015 by xi'an

Another surreal experience thanks to X validated! A user of the forum recently asked for an explanation of the above proof in Lynch’s (2007) book, Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. No wonder this user was puzzled: the explanation makes no sense outside the univariate case… It is hard to fathom why on Earth the author would resort to this convoluted approach to conclude about the posterior conditional distribution being a normal centred at the least square estimate and with σ²X’X as precision matrix. Presumably, he has a poor opinion of the degree of matrix algebra numeracy of his readers [and thus should abstain from establishing the result]. As it seems unrealistic to postulate that the author is himself confused about matrix algebra, given his MSc in Statistics [the footnote ² seen above after “appropriately” acknowledges that “technically we cannot divide by” the matrix, but it goes on to suggest multiplying the numerator by the matrix

$(X^\text{T}X)^{-1} (X^\text{T}X)$

which does not make sense either, unless one introduces the trace tr(.) operator, presumably out of reach for most readers]. And this part of the explanation is unnecessarily confusing in that a basic matrix manipulation leads to the result. Or even simpler, a reference to Pythagoras’  theorem.

## independent component analysis and p-values

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , on September 8, 2014 by xi'an

Last morning at the neuroscience workshop Jean-François Cardoso presented independent component analysis though a highly pedagogical and enjoyable tutorial that stressed the geometric meaning of the approach, summarised by the notion that the (ICA) decomposition

$X=AS$

of the data X seeks both independence between the columns of S and non-Gaussianity. That is, getting as away from Gaussianity as possible. The geometric bits came from looking at the Kullback-Leibler decomposition of the log likelihood

$-\mathbb{E}[\log L(\theta|X)] = KL(P,Q_\theta) + \mathfrak{E}(P)$

where the expectation is computed under the true distribution P of the data X. And Qθ is the hypothesised distribution. A fine property of this decomposition is a statistical version of Pythagoreas’ theorem, namely that when the family of Qθ‘s is an exponential family, the Kullback-Leibler distance decomposes into

$KL(P,Q_\theta) = KL(P,Q_{\theta^0}) + KL(Q_{\theta^0},Q_\theta)$

where θ⁰ is the expected maximum likelihood estimator of θ. (We also noticed this possibility of a decomposition in our Kullback-projection variable-selection paper with Jérôme Dupuis.) The talk by Aapo Hyvärinen this morning was related to Jean-François’ in that it used ICA all the way to a three-level representation if oriented towards natural vision modelling in connection with his book and the paper on unormalised models recently discussed on the ‘Og.

On the afternoon, Eric-Jan Wagenmaker [who persistently and rationally fight the (ab)use of p-values and who frequently figures on Andrew’s blog] gave a warning tutorial talk about the dangers of trusting p-values and going fishing for significance in existing studies, much in the spirit of Andrew’s blog (except for the defence of Bayes factors). Arguing in favour of preregistration. The talk was full of illustrations from psychology. And included the line that ESP testing is the jester of academia, meaning that testing for whatever form of ESP should be encouraged as a way to check testing procedures. If a procedure finds a significant departure from the null in this setting, there is something wrong with it! I was then reminded that Eric-Jan was one of the authors having analysed Bem’s controversial (!) paper on the “anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms”… (And of the shocking talk by Jessica Utts on the same topic I attended in Australia two years ago.)

## Mathematics and realism

Posted in Books with tags , , , , , , , , , , , on November 27, 2010 by xi'an

I read in Liberation a rather surprising tribune (in French) by “Yann Moix, writer”. The starting point is a criticism of Stephen Hawking (and Leonard Mlodinow)’s recent book The Grand Design, With regards to its conclusion that a god is not necessary to explain the creation and the working of the Universe: “It is not necessary to invoke God to light the blue touch paper and set the universe going.” I haven’t read Hawking’s book (although I briefly considered buying it in London last time I was there, here is a Guardian review), I had never heard before of this (controversial) writer, and I do not see the point in debating about supernatural beings (except when reviewing a fantasy book!). However, the arguments of Moix are rather limited from a philosophical viewpoint.