## intuition beyond a Beta property

Posted in Books, Kids, R, Statistics, University life with tags , , , on March 30, 2015 by xi'an

A self-study question on X validated exposed an interesting property of the Beta distribution:

If x is B(n,m) and y is B(n+½,m) then √xy is B(2n,2m)

While this can presumably be established by a mere change of variables, I could not carry the derivation till the end and used instead the moment generating function E[(XY)s/2] since it naturally leads to ratios of B(a,b) functions and to nice cancellations thanks to the ½ in some Gamma functions [and this was the solution proposed on X validated]. However, I wonder at a more fundamental derivation of the property that would stem from a statistical reasoning… Trying with the ratio of Gamma random variables did not work. And the connection with order statistics does not apply because of the ½. Any idea?

## off to New York

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , on March 29, 2015 by xi'an

I am off to New York City for two days, giving a seminar at Columbia tomorrow and visiting Andrew Gelman there. My talk will be about testing as mixture estimation, with slides similar to the Nice ones below if slightly upgraded and augmented during the flight to JFK. Looking at the past seminar speakers, I noticed we were three speakers from Paris in the last fortnight, with Ismael Castillo and Paul Doukhan (in the Applied Probability seminar) preceding me. Is there a significant bias there?!

## a most curious case of misaddressed mail

Posted in Books, Travel, University life with tags , , , , , , , , on March 28, 2015 by xi'an

Today, I got two FedEx envelopes in the mail, both apparently from the same origin, namely UF Statistics department reimbursing my travel expenses. However, once both envelopes opened, I discovered that, while one was indeed containing my reimbursement cheque, the other one contained several huge cheques addressed to… a famous Nova Scotia fiddler, Natalie MacMaster, for concerts she gave recently in South East US, and with no possible connection with either me or the stats department! So I have no idea how those cheques came to me (before I returned them to their rightful recipient in Nova Scotia!). Complete mystery! The only possible link is that I just found Natalie MacMaster and her band played in Gainesville two weeks ago. Hence a potential scenario: at the local FedEx sorting centre, the envelope intended for Natalie MacMaster lost its label and someone took the second label from my then nearby envelope to avoid dealing with the issue…  In any case, this gave me the opportunity to listen to pretty enticing Scottish music!

## likelihood-free model choice

Posted in Books, pictures, Statistics, University life, Wines with tags , , , , , , , on March 27, 2015 by xi'an

Jean-Michel Marin, Pierre Pudlo and I just arXived a short review on ABC model choice, first version of a chapter for the incoming Handbook of Approximate Bayesian computation edited by Scott Sisson, Yannan Fan, and Mark Beaumont. Except for a new analysis of a Human evolution scenario, this survey mostly argues for the proposal made in our recent paper on the use of random forests and [also argues] about the lack of reliable approximations to posterior probabilities. (Paper that was rejected by PNAS and that is about to be resubmitted. Hopefully with a more positive outcome.) The conclusion of the survey is  that

The presumably most pessimistic conclusion of this study is that the connections between (i) the true posterior probability of a model, (ii) the ABC version of this probability, and (iii) the random forest version of the above, are at best very loose. This leaves open queries for acceptable approximations of (i), since the posterior predictive error is instead an error assessment for the ABC RF model choice procedure. While a Bayesian quantity that can be computed at little extra cost, it does not necessarily compete with the posterior probability of a model.

reflecting my hope that we can eventually come up with a proper approximation to the “true” posterior probability…

## importance weighting without importance weights [ABC for bandits?!]

Posted in Books, Statistics, University life with tags , , , , on March 27, 2015 by xi'an

I did not read very far in the recent arXival by Neu and Bartók, but I got the impression that it was a version of ABC for bandit problems where the probabilities behind the bandit arms are not available but can be generated. Since the stopping rule found in the “Recurrence weighting for multi-armed bandits” is the generation of an arm equal to the learner’s draw (p.5). Since there is no tolerance there, the method is exact (“unbiased”). As no reference is made to the ABC literature, this may be after all a mere analogy…

## the maths of Jeffreys-Lindley paradox

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

Cristiano Villa and Stephen Walker arXived on last Friday a paper entitled On the mathematics of the Jeffreys-Lindley paradox. Following the philosophical papers of last year, by Ari Spanos, Jan Sprenger, Guillaume Rochefort-Maranda, and myself, this provides a more statistical view on the paradox. Or “paradox”… Even though I strongly disagree with the conclusion, namely that a finite (prior) variance σ² should be used in the Gaussian prior. And fall back on classical Type I and Type II errors. So, in that sense, the authors avoid the Jeffreys-Lindley paradox altogether!

The argument against considering a limiting value for the posterior probability is that it converges to 0, 21, or an intermediate value. In the first two cases it is useless. In the medium case. achieved when the prior probability of the null and alternative hypotheses depend on variance σ². While I do not want to argue in favour of my 1993 solution

$\rho(\sigma) = 1\big/ 1+\sqrt{2\pi}\sigma$

since it is ill-defined in measure theoretic terms, I do not buy the coherence argument that, since this prior probability converges to zero when σ² goes to infinity, the posterior probability should also go to zero. In the limit, probabilistic reasoning fails since the prior under the alternative is a measure not a probability distribution… We should thus abstain from over-interpreting improper priors. (A sin sometimes committed by Jeffreys himself in his book!)

## Le Monde puzzle [#904.5]

Posted in Books, Kids, R, Statistics, University life with tags , , , on March 25, 2015 by xi'an

Find all plural integers, namely positive integers such that (a) none of their digits is zero and (b) removing their leftmost digit produces a dividing plural integer (with the convention that one digit integers are all plural).

a slight modification in the R code allows for a faster exploration, based on the fact that solutions add one extra digit to solutions with one less digit:

First, I found this function on Stack Overflow to turn an integer into its digits:

pluri=plura=NULL
#solutions with two digits
for (i in 11:99){

dive=rev(digin(i)[-1])
if (min(dive)&gt;0){
dive=sum(dive*10^(0:(length(dive)-1)))
if (i==((i%/%dive)*dive))
pluri=c(pluri,i)}}

for (n in 2:6){ #number of digits
plura=c(plura,pluri)
pluro=NULL
for (j in pluri){

for (k in (1:9)*10^n){
x=k+j
if (x==(x%/%j)*j)
pluro=c(pluro,x)}
}
pluri=pluro}

which leads to the same output

&gt; sort(plura)
[1] 11 12 15 21 22 24 25 31 32 33 35 36
[13] 41 42 44 45 48 51 52 55 61 62 63 64
[25] 65 66 71 72 75 77 81 82 84 85 88 91
[37] 92 93 95 96 99 125 225 312 315 325 375 425
[49] 525 612 615 624 625 675 725 735 825 832 912
[61] 915 925 936 945 975 1125 2125 3125 3375 4125
[70] 5125 5625
[72] 6125 6375 7125 8125 9125 9225 9375 53125
[80] 91125 95625