Archive for power

midnight run

Posted in Running, Travel with tags , , , , , , , , on December 8, 2019 by xi'an

Le Monde puzzle [#1114]

Posted in Kids, R with tags , , , , , , on October 16, 2019 by xi'an

Another very low-key arithmetic problem as Le Monde current mathematical puzzle:

32761 is 181² and the difference of two cubes, which ones? And 181=9²+10², the sum of two consecutive integers. Is this a general rule, i.e. the root z of a perfect square that is the difference of two cubes is always the sum of two consecutive integers squared?

The solution proceeds by a very dumb R search of cubes, leading to

34761=105³-104³

The general rule can be failed by a single counter-example. Running

sol=0;while(!sol){
  x=sample(2:1e3,1)
  y=sample(1:x,1)-1
  sol=is.sqr(z<-x^3-y^3)
  z=round(sqrt(z))
  if (sol) 
   sol=(trunc(sqrt(z/2))^2+ceiling(sqrt(z/2))^2!=z)}

which is based on the fact that, if z is the sum of two consecutive integers squared, a² and (a+1)² then

2 a²<z<2 (a+1)²

Running the R code produces

x=14, y=7

as a counter-example. (Note that, however, if the difference of cubes of two consecutive integers is a square, then this square can be written as the sum of the squares of two different integers.) Reading the solution in the following issue led me to realise I had missed the consecutive in the statement of the puzzle!

How many subjects? [not a book review]

Posted in Books, pictures, Statistics with tags , , , , , , on September 24, 2018 by xi'an

take those hats off [from R]!

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , on May 5, 2015 by xi'an

from my office, La Défense & Bois de Boulogne, Paris, May 15, 2012This is presumably obvious to most if not all R programmers, but I became aware today of a hugely (?) delaying tactic in my R codes. I was working with Jean-Michel and Natesh [who are visiting at the moment] and when coding an MCMC run I was telling them that I usually preferred to code Nsim=10000 as Nsim=10^3 for readability reasons. Suddenly, I became worried that this representation involved a computation, as opposed to Nsim=1e3 and ran a little experiment:

> system.time(for (t in 1:10^8) x=10^3)
utilisateur     système      écoulé
     30.704       0.032      30.717
> system.time(for (t in 1:1e8) x=10^3)
utilisateur     système      écoulé
     30.338       0.040      30.359
> system.time(for (t in 1:10^8) x=1000)
utilisateur     système      écoulé
      6.548       0.084       6.631
> system.time(for (t in 1:1e8) x=1000)
utilisateur     système      écoulé
      6.088       0.032       6.115
> system.time(for (t in 1:10^8) x=1e3)
utilisateur     système      écoulé
      6.134       0.029       6.157
> system.time(for (t in 1:1e8) x=1e3)
utilisateur     système      écoulé
      6.627       0.032       6.654
> system.time(for (t in 1:10^8) x=exp(3*log(10)))
utilisateur     système      écoulé
     60.571        0.000     57.103

 So using the usual scientific notation with powers is taking its toll! While the calculator notation with e is cost free… Weird!

I understand that the R notation 10^6 is an abbreviation for a power function that can be equally applied to pi^pi, say, but still feel aggrieved that a nice scientific notation like 10⁶ ends up as a computing trap! I thus asked the question to the Stack Overflow forum, getting the (predictable) answer that the R code 10^6 meant calling the R power function, while 1e6 was a constant. Since 10⁶ does not differ from ππ, there is no reason 10⁶ should be recognised by R as a million. Except that it makes my coding more coherent.

> system.time( for (t in 1:10^8) x=pi^pi)
utilisateur     système      écoulé
     44.518       0.000      43.179
> system.time( for (t in 1:10^8) x=10^6)
utilisateur     système      écoulé
     38.336       0.000      37.860

Another thing I discovered from this answer to my question is that negative integers are also requesting call to a function:

> system.time( for (t in 1:10^8) x=1)
utilisateur     système      écoulé
     10.561       0.801      11.062
> system.time( for (t in 1:10^8) x=-1)
utilisateur     système      écoulé
     22.711       0.860      23.098

This sounds even weirder.

straightforward statistics [book review]

Posted in Books, Kids, Statistics, University life with tags , , , , , , , on July 3, 2014 by xi'an

“I took two different statistics courses as an undergraduate psychology major [and] four different advanced statistics classes as a PhD student.” G. Geher

Straightforward Statistics: Understanding the Tools of Research by Glenn Geher and Sara Hall is an introductory textbook for psychology and other social science students. (That Oxford University Press sent me for review in CHANCE. Nice cover, by the way!) I can spot the purpose behind the title, purpose heavily stressed anew in the preface and the first chapter, but it nonetheless irks me as conveying the message that one semester of reasonable diligence in class will suffice to any college students to “not only understanding research findings from psychology, but also to uncovering new truths about the world and our place in it” (p.9). Nothing less. While, in essence, it covers the basics found in all introductory textbooks, from descriptive statistics to ANOVA models. The inclusion of “real research examples” in the chapters of the book rather demonstrates how far from real research a reader of the book would stand… Continue reading

uniformly most powerful Bayesian tests???

Posted in Books, Statistics, University life with tags , , , , , , , on September 30, 2013 by xi'an

“The difficulty in constructing a Bayesian hypothesis test arises from the requirement to specify an alternative hypothesis.”

Vale Johnson published (and arXived) a paper in the Annals of Statistics on uniformly most powerful Bayesian tests. This is in line with earlier writings of Vale on the topic and good quality mathematical statistics, but I cannot really buy the arguments contained in the paper as being compatible with (my view of) Bayesian tests. A “uniformly most powerful Bayesian test” (acronymed as UMBT)  is defined as

“UMPBTs provide a new form of default, nonsubjective Bayesian tests in which the alternative hypothesis is determined so as to maximize the probability that a Bayes factor exceeds a specified threshold”

which means selecting the prior under the alternative so that the frequentist probability of the Bayes factor exceeding the threshold is maximal for all values of the parameter. This does not sound very Bayesian to me indeed, due to this averaging over all possible values of the observations x and comparing the probabilities for all values of the parameter θ rather than integrating against a prior or posterior and selecting the prior under the alternative with the sole purpose of favouring the alternative, meaning its further use when the null is rejected is not considered at all and catering to non-Bayesian theories, i.e. trying to sell Bayesian tools as supplementing p-values and arguing the method is objective because the solution satisfies a frequentist coverage (at best, this maximisation of the rejection probability reminds me of minimaxity, except there is no clear and generic notion of minimaxity in hypothesis testing).

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Olli à/in/im Paris

Posted in Statistics, Travel, University life with tags , , , , , , , , , , , , on May 27, 2013 by xi'an

Warning: Here is an old post from last October I can at last post since Olli just arXived the paper on which this talk was based (more to come, before or after Olli’s talk in Roma!).

Oliver Ratman came to give a seminar today at our Big’MC seminar series. It was an extension of the talk I attended last month in Bristol:

10:45 Oliver Ratmann (Duke University and Imperial College) – “Approximate Bayesian Computation based on summaries with frequency properties”

Approximate Bayesian Computation (ABC) has quickly become a valuable tool in many applied fields, but the statistical properties obtained by choosing a particular summary, distance function and error threshold are poorly understood. In an effort to better understand the effect of these ABC tuning parameters, we consider summaries that are associated with empirical distribution functions. These frequency properties of summaries suggest what kind of distance function are appropriate, and the validity of the choice of summaries can be assessed on the fly during Monte Carlo simulations. Among valid choices, uniformly most powerful distances can be shown to optimize the ABC acceptance probability. Considering the binding function between the ABC model and the frequency model of the summaries, we can characterize the asymptotic consistency of the ABC maximum-likelhood estimate in general situations. We provide examples from phylogenetics and dynamical systems to demonstrate that empirical distribution functions of summaries can often be obtained without expensive re-simulations, so that the above theoretical results are applicable in a broad set of applications. In part, this work will be illustrated on fitting phylodynamic models that capture the evolution and ecology of interpandemic influenza A (H3N2) to incidence time series and the phylogeny of H3N2’s immunodominant haemagglutinin gene.

I however benefited enormously from hearing the talk again and also from discussing the fundamentals of his approach before and after the talk (in the nearest Aussie pub!). Olli’s approach is (once again!) rather iconoclastic in that he presents ABC as a testing procedure, using frequentist tests and concepts to build an optimal acceptance condition. Since he manipulates several error terms simultaneously (as before), he needs to address the issue of multiple testing but, thanks to a switch between acceptance and rejection, null and alternative, the individual α-level tests get turned into a global α-level test.