## the French MIT? not so fast…

Posted in Kids, pictures, University life with tags , , , , , , , on February 20, 2017 by xi'an

A news report last weekend on Nature webpage about the new science super-campus south of Paris connected with my impressions of the whole endeavour: the annual report from the Court of Auditors estimated that the 5 billion euros invested in this construct were not exactly a clever use of public [French taxpayer] money! This notion to bring a large number of [State] engineer and scientific schools from downtown Paris to the plateau of Saclay, about 25km south-west of Paris, around École Polytechnique, had some appeal, since these were and are prestigious institutions, most with highly selective entry exams, and with similar training programs, now that they have almost completely lost the specialisation that justified their parallel existences! And since a genuine university, Paris 11 Orsay, stood nearby at the bottom of the plateau. Plus, a host of startups and research branches of companies. Hence the concept of a French MIT.

However, as so often the case in Jacobin France, the move has been decided and supported by the State “top-down” rather than by the original institutions themselves. Including a big push by Nicolas Sarkozy in 2010. While the campus can be reached by public transportation like RER, the appeal of living and working on the campus is obviously less appealing to both students and staff than in a listed building in the centre of Paris. Especially when lodging and living infrastructures are yet to be completed. But the main issue is that the fragmentation of those schools, labs and institutes, in terms of leadership, recruiting, research, and leadership, has not been solved by the move, each entity remaining strongly attached to its identity, degree, networks, &tc., and definitely unwilling to merge into a super-university with a more efficient organisation of teaching and research. Which means the overall structure as such is close to invisible at the international level. This is the point raised by the State auditors. And perceived by the State which threatens to cut funding at this late stage!

This is not the only example within French higher educations institutions since most have been forced to merged into incomprehensible super-units under the same financial threat. Like Paris-Dauphine being now part of the PSL (Paris Sciences et Lettres) heterogeneous conglomerate. (I suspect one of the primary reasons for this push by central authorities was to create larger entities towards moving up in the international university rankings, which is absurd for many reasons, from the limited worth of such rankings, to the lag between the creation of a new entity and the appearance on an international university ranking, to the difficulty in ranking researchers from such institutions: in Paris-Dauphine, the address to put on papers is more than a line long, with half a dozen acronyms!)

## 神々の山嶺 [the summit of the gods]

Posted in Books, Kids, Mountains, pictures with tags , , , , , , , , , , , on February 19, 2017 by xi'an

The summit of the gods is a five volume manga created by Jiro Taniguchi, who just passed away. While I do not find the mountaineering part of the story realistic [as in the above stripe], with feats and strength that seem beyond even the top himalayists like Reinhold Messner, Pierre Beghin, Abele Blanc, or Ueli Steck (to name a few), I keep re-reading the series for the unique style of the drawing, the story (despite the above), and the atmosphere of solo climbing in the 1970’s or 1980’s, especially as a testimony to Japanese climbers, as well as the perfect rendition of the call of the mountains… Reading Taniguchi’s obituaries over the weekend, I realised he was much more popular in France, where he won a prize for his drawing at the BD Festival in Angoulême in 2005, than in Japan.

## A knapsack riddle [#2]?

Posted in Kids, R, Statistics with tags , , , on February 17, 2017 by xi'an

Still about this allocation riddle of the past week, and still with my confusion about the phrasing of the puzzle, when looking at a probabilistic interpretation of the game, rather than for a given adversary’s y, the problem turns out to search for the maximum of

$\mathbb{E}[L(x,Y)]=\sum_{i=1}^{10} i\{P(Y_ix_i)\}$

where the Y’s are Binomial B(100,p). Given those p’s, this function of x is available in closed form and can thus maximised by a simulated annealing procedure, coded as

utility=function(x,p){
ute=2*pbinom(x[1]-1,100,prob=p[1])+
dbinom(x[1],100,p[1])
for (i in 2:10)
ute=ute+2*i*pbinom(x[i]-1,100,prob=p[i])+
i*dbinom(x[i],100,p[i])
return(ute)}
#basic term in utility
baz=function(i,x,p){
return(i*dbinom(x[i],100,p[i])+
i*dbinom(x[i]-1,100,p[i]))}
#relies on a given or estimated p
x=rmultinom(n=1,siz=100,prob=p)
maxloz=loss=0
newloss=losref=utility(x,p)
#random search
T=1e3
Te=1e2
baza=rep(0,10)
t=1
while ((t<T)||(newloss>loss)){
loss=newloss
i=sample(1:10,1,prob=(10:1)*(x>0))
#moving all other counters by one
xp=x+1;xp[i]=x[i]
#corresponding utility change
for (j in 1:10) baza[j]=baz(j,xp,p)
proz=exp(log(t)*(baza-baza[i])/Te)
#soft annealing move
j=sample(1:10,1,prob=proz)
if (i!=j){ x[i]=x[i]-1;x[j]=x[j]+1}
newloss=loss+baza[j]-baza[i]
if (newloss>maxloz){
maxloz=newloss;argz=x}
t=t+1
if ((t>T-10)&(newloss<losref)){
t=1;loss=0
x=rmultinom(n=1,siz=100,prob=p)
newloss=losref=utility(x,p)}}


which seems to work, albeit not always returning the same utility. For instance,

> p=cy/sum(cy)
> utility(argz,p)
[1] 78.02
> utility(cy,p)
[1] 57.89


or

> p=sy/sum(sy)
> utility(argz,p)
[1] 82.04
> utility(sy,p)
[1] 57.78


Of course, this does not answer the question as intended and reworking the code to that purpose is not worth the time!

## Les Diablerets skyline [jatp]

Posted in Kids, Mountains, pictures, Statistics, Travel with tags , , , , , , , on February 12, 2017 by xi'an

The short course I gave in Les Diablerets, Switzerland, was highly enjoyable, at least for me!, as it gave me the opportunity to present an  overview of the field, just before our workshop in Banff and to stay in a fantastic skiing area for four days! While I found out that my limited skiing skills are gone even more limited, the constant fresh snow falling during my stay and the very small number of people on the slopes made the outdoor highly enjoyable, the more because the temperatures were quite tolerable. The above picture was taken on the only morning it did not snow, with a nice cloud inversion over the valley separating France and Switzerland.

## Oxford snapshot [jatp]

Posted in Books, Kids, pictures, Travel, University life with tags , , , , , , on February 9, 2017 by xi'an

## an accurate variance approximation

Posted in Books, Kids, pictures, R, Statistics with tags , , , , , , on February 7, 2017 by xi'an

In answering a simple question on X validated about producing Monte Carlo estimates of the variance of estimators of exp(-θ) in a Poisson model, I wanted to illustrate the accuracy of these estimates against the theoretical values. While one case was easy, since the estimator was a Binomial B(n,exp(-θ)) variate [in yellow on the graph], the other one being the exponential of the negative of the Poisson sample average did not enjoy a closed-form variance and I instead used a first order (δ-method) approximation for this variance which ended up working surprisingly well [in brown] given that the experiment is based on an n=20 sample size.

Thanks to the comments of George Henry, I stand corrected: the variance of the exponential version is easily manageable with two lines of summation! As

$\text{var}(\exp\{-\bar{X}_n\})=\exp\left\{-n\theta[1-\exp\{-2/n\}]\right\}$

$-\exp\left\{-2n\theta[1-\exp\{-1/n\}]\right\}$

which allows for a comparison with its second order Taylor approximation:

## a well-hidden E step

Posted in Books, Kids, pictures, R, Statistics with tags , , , , , , , , , on February 3, 2017 by xi'an

A recent question on X validated ended up being quite interesting! The model under consideration is made of parallel Markov chains on a finite state space, all with the same Markov transition matrix, M, which turns into a hidden Markov model when the only summary available is the number of chains in a given state at a given time. When writing down the EM algorithm, the E step involves the expected number of moves from a given state to a given state at a given time. The conditional distribution of those numbers of chains is a product of multinomials across times and starting states, with no Markov structure since the number of chains starting from a given state is known at each instant. Except that those multinomials are constrained by the number of “arrivals” in each state at the next instant and that this makes the computation of the expectation intractable, as far as I can see.

A solution by Monte Carlo EM means running the moves for each instant under the above constraints, which is thus a sort of multinomial distribution with fixed margins, enjoying a closed-form expression but for the normalising constant. The direct simulation soon gets too costly as the number of states increases and I thus considered a basic Metropolis move, using one margin (row or column) or the other as proposal, with the correction taken on another margin. This is very basic but apparently enough for the purpose of the exercise. If I find time in the coming days, I will try to look at the ABC resolution of this problem, a logical move when starting from non-sufficient statistics!