Archive for the Books Category

random walk on a torus [riddle]

Posted in Books, Kids, pictures with tags , , , , , , , , , on September 16, 2016 by xi'an

Galgate, Lancastershire, July 19, 2011The Riddler of this week(-end) has a simple riddle to propose, namely given a random walk on the {1,2,…,N} torus with a ⅓ probability of death, what is the probability of death occurring at the starting point?

The question is close to William Feller’s famous Chapter III on random walks. With his equally famous reflection principle. Conditioning on the time n of death, which as we all know is definitely absorbing (!), the event of interest is a passage at zero, or any multiple of N (omitting the torus cancellation), at time n-1 (since death occurs the next time). For a passage in zero, this does not happen if n is even (since n-1 is odd) and else it is a Binomial event with probability

{n \choose \frac{n-1}{2}} 2^{-n}

For a passage in kN, with k different from zero, kN+n must be odd and the probability is then

{n \choose \frac{n-1+kN}{2}} 2^{-n}

which leads to a global probability of

\sum_{n=0}^\infty \dfrac{2^n}{3^{n+1}} \sum_{k=-\lfloor (n-1)/N \rfloor}^{\lfloor (n+1)/N \rfloor} {n \choose \frac{n-1+kN}{2}} 2^{-n}


\sum_{n=0}^\infty \dfrac{1}{3^{n+1}} \sum_{k=-\lfloor (n-1)/N \rfloor}^{\lfloor (n+1)/N \rfloor} {n \choose \frac{n-1+kN}{2}}

Since this formula is rather unwieldy I looked for another approach in a métro ride [to downtown Paris to enjoy a drink with Stephen Stiegler]. An easier one is to allocate to each point on the torus a probability p[i] to die at position 1 and to solve the system of equations that is associated with it. For instance, when N=3, the system of equations is reduced to

p_0=1/3+2/3 p_1, \quad p_1=1/3 p_0 + 1/3 p_1

which leads to a probability of ½ to die at position 0 when leaving from 0. When letting N grows to infinity, the torus structure no longer matters and the probability of dying at position 0 implies returning in position 0, which is a special case of the above combinatoric formula, namely

\sum_{m=0}^\infty \dfrac{1}{3^{2m+1}}  {2m \choose m}

which happens to be equal to

\dfrac{1}{3}\,\dfrac{1}{\sqrt{1-4/9}}=\dfrac{1}{\sqrt{5}}\approx 0.4472

as can be [unnecessarily] checked by a direct R simulation. This √5 is actually the most surprising part of the exercise!

local kernel reduction for ABC

Posted in Books, pictures, Statistics, University life with tags , , , , , on September 14, 2016 by xi'an

“…construction of low dimensional summary statistics can be performed as in a black box…”

Today Zhou and Fukuzumi just arXived a paper that proposes a gradient-based dimension reduction for ABC summary statistics, in the spirit of RKHS kernels as advocated, e.g., by Arthur Gretton. Here the projection is a mere linear projection Bs of the vector of summary statistics, s, where B is an estimated Hessian matrix associated with the posterior expectation E[θ|s]. (There is some connection with the latest version of Li’s and Fearnhead’s paper on ABC convergence as they also define a linear projection of the summary statistics, based on asymptotic arguments, although their matrix does depend on the true value of the parameter.) The linearity sounds like a strong restriction [to me] especially when the summary statistics have no reason to belong to a vectorial space and thus be open to changes of bases and linear projections. For instance, a specific value taken by a summary statistic, like 0 say, may be more relevant than the range of their values. On a larger scale, I am doubtful about always projecting a vector of summary statistics on a subspace with the smallest possible dimension, ie the dimension of θ. In practical settings, it seems impossible to derive the optimal projection and a subvector is almost certain to loose information against a larger vector.

“Another proposal is to use different summary statistics for different parameters.”

Which is exactly what we did in our random forest estimation paper. Using a different forest for each parameter of interest (but no real tree was damaged in the experiment!).

Savage-Dickey supermodels

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on September 13, 2016 by xi'an

The Wider Image: Bolivia's cholita climbers: Combination picture shows Aymara indigenous women (L-R) Domitila Alana, 42, Bertha Vedia, 48, Lidia Huayllas, 48, and Dora Magueno, 50, posing for a photograph at the Huayna Potosi mountain, Bolivia April 6, 2016Combination picture shows Aymara indigenous women (L-R) Domitila Alana, 42, Bertha Vedia, 48, Lidia Huayllas, 48, and Dora Magueno, 50, posing for a photograph at the Huayna Potosi mountain, Bolivia April 6, 2016. (c.) REUTERS/David Mercado. REUTERS/David MercadoA. Mootoovaloo, B. Bassett, and M. Kunz just arXived a paper on the computation of Bayes factors by the Savage-Dickey representation through a supermodel (or encompassing model). (I wonder why Savage-Dickey is so popular in astronomy and cosmology statistical papers and not so much elsewhere.) Recall that the trick is to write the Bayes factor in favour of the encompasssing model as the ratio of the posterior and of the prior for the tested parameter (thus eliminating nuisance or common parameters) at its null value,


Modulo some continuity constraints on the prior density, and the assumption that the conditional prior on nuisance parameter is the same under the null model and the encompassing model [given the null value φ⁰]. If this sounds confusing or even shocking from a mathematical perspective, check the numerous previous entries on this topic on the ‘Og!

The supermodel created by the authors is a mixture of the original models, as in our paper, and… hold the presses!, it is a mixture of the likelihood functions, as in Phil O’Neill’s and Theodore Kypraios’ paper. Which is not mentioned in the current paper and should obviously be. In the current representation, the posterior distribution on the mixture weight α is a linear function of α involving both evidences, α(m¹-m²)+m², times the artificial prior on α. The resulting estimator of the Bayes factor thus shares features with bridge sampling, reversible jump, and the importance sampling version of nested sampling we developed in our Biometrika paper. In addition to O’Neill and Kypraios’s solution.

The following quote is inaccurate since the MCMC algorithm needs simulating the parameters of the compared models in realistic settings, hence representing the multidimensional integrals by Monte Carlo versions.

“Though we have a clever way of avoiding multidimensional integrals to calculate the Bayesian Evidence, this new method requires very efficient sampling and for a small number of dimensions is not faster than individual nested sampling runs.”

I actually wonder at the sheer rationale of running an intensive MCMC sampler in such a setting, when the weight α is completely artificial. It is only used to jump from one model to the next, which sound quite inefficient when compared with simulating from both models separately and independently. This approach can also be seen as a special case of Carlin’s and Chib’s (1995) alternative to reversible jump. Using instead the Savage-Dickey representation is of course infeasible. Which makes the overall reference to this method rather inappropriate in my opinion. Further, the examples processed in the paper all involve (natural) embedded models where the original Savage-Dickey approach applies. Creating an additional model to apply a pseudo-Savage-Dickey representation does not sound very compelling…

Incidentally, the paper also includes a discussion of a weird notion, the likelihood of the Bayes factor, B¹², which is plotted as a distribution in B¹², most strangely. The only other place I met this notion is in Murray Aitkin’s book. Something’s unclear there or in my head!

“One of the fundamental choices when using the supermodel approach is how to deal with common parameters to the two models.”

This is an interesting question, although maybe not so relevant for the Bayes factor issue where it should not matter. However, as in our paper, multiplying the number of parameters in the encompassing model may hinder convergence of the MCMC chain or reduce the precision of the approximation of the Bayes factor. Again, from a Bayes factor perspective, this does not matter [while it does in our perspective].

[Royal] Series B’log

Posted in Books, Statistics, University life, Wines with tags , , , , , , on September 12, 2016 by xi'an

[Thanks to Ingmar for suggesting the additional Royal!]

RSS wineLast week, I got an email from Piotr Fryzlewicz on behalf of the Publication Committee of the Royal Statistical Society enquiring about my interest in becoming a blog associate editor for Series B! Although it does not come exactly as a surprise, as I had previously heard about this interest in creating a dedicated blog, this is great news as I think a lively blog can only enhance the visibility and impact of papers published in Series B and hence increase the influence of Series B. Being quite excited by this on-line and interactive extension to the journal, I have accepted the proposal and we are now working on designing the new blog (Series B’log!) to get it on track as quickly as possible.

Suggestions towards this experiment are most welcome! I am thinking of involving authors to write blog summaries of their paper, AEs and reviewers to voice their expert opinions about the paper, anonymously or not, and of course anyone interested in commenting the paper. The idea is to turn (almost) all papers into on-line Read Papers, with hopefully the backup of authors through their interactions with the commentators. I certainly do not intend to launch discussions on each and every paper, betting on the AEs or referees to share their impressions. And if a paper ends up being un-discussed, this may prove enough of an incentive for some. (Someone asked me if we intended to discuss rejected papers as well. This is an interesting concept, but not to be considered at the moment!)

The one-hundred year old man who climbed out of the window and disappeared [book review]

Posted in Books with tags , , , , , , , , on September 11, 2016 by xi'an

Scandinavian picaresque, in the spirit of the novels of Paasilinna, and following another book by Jonas Jonasson already commented on the ‘Og, The Girl who saved the King of Sweden, but not as funny, because of the heavy recourse to World history, the main (100 year old) character meeting a large collection of major historical figures. And crossing the Himalayas when escaping from a Russian Gulag, which reminded me of this fantastic if possibly apocryphal The Long Walk where a group of Polish prisoners was making it through the Gobi desert to reach India and freedom (or death). The story here is funny but not that funny and once it is over, there is not much to say about it, which is why I left it on a bookshare table in Monash. The current events are somewhat dull, in opposition to the 100 year life of Allan, and the police enquiry a tad too predictable. Plus the themes are somewhat comparable to The Girl who …, with atom bombs, cold war, brothers hating one another…

Darwin’s radio [book review]

Posted in Books, Kids, pictures, University life with tags , , , , , , , , , , , , , , , , on September 10, 2016 by xi'an

When in Sacramento two weeks ago I came across the Beers Books Center bookstore, with a large collection of used and (nearly) new cheap books and among other books I bought Greg Bear’s Darwin Radio. I had (rather) enjoyed another book of his’, Hull Zero Three, not to mention one of his first books, Blood Music, I read in the mid 1980’s, and the premises of this novel sounded promising, not mentioning the Nebula award. The theme is of a major biological threat, apparently due to a new virus, and of the scientific unraveling of what the threat really means. (Spoilers alert!) In that respect it sounds rather similar to the (great) Crichton‘s The Andromeda Strain, which is actually mentioned by some characters in this book. As is Ebola, as a sort of contrapoint (since Ebola is a deadly virus, although the epidemic in Western Africa now seems to have vanished). The biological concept exploited here is dormant DNA in non-coding parts of the genome that periodically get awaken and induce massive steps in the evolution. So massive that carriers of those mutations are killed by locals. Until the day it happens in an all-connected World and the mutation can no longer be stopped. The concept is compelling if not completely convincing of course, while the outcome of a new human race, which is to Homo Sapiens what Homo Sapiens was to Neanderthal, is rather disappointing. (How could it be otherwise?!) But I did appreciate the postulate of a massive and immediate change in the genome, even though the details were disputable and the dismissal of Dawkins‘ perspective poorly defended. From a stylistic perspective, the style is at time heavy, while there are too many chance occurrences, like the main character happening to be in Georgia for a business deal (spoilers, spoilers!) at the times of the opening of collective graves, or the second main character coming upon a couple of Neanderthal mummies with a Sapiens baby, or yet this pair of main characters falling in love and delivering a live mutant baby-girl. But I enjoyed reading it between San Francisco and Melbourne, with a few hours of lost sleep and work. It is a page turner, no doubt! I also like the political undercurrents, from riots to emergency measures, to an effective dictatorship controlling pregnancies and detaining newborns and their mothers.

One important thread in the book deals with anthropology digs getting against Native claims to corpses and general opposition to such digs. This reminded me of a very recent article in Nature where a local Indian tribe had claimed rights to several thousand year old skeletons, whose DNA was then showed to be more related with far away groups than the claimants. But where the tribe was still granted the last word, in a rather worrying jurisprudence.

Jeff down-under

Posted in Books, Statistics, Travel, University life with tags , , , , , , , on September 9, 2016 by xi'an

amsi_ssaJeff Rosenthal is the AMSI-SSA (Australia Mathematical Sciences Institute – Statistical Society of Australia) lecturer this year and, as I did in 2012, will tour Australia giving seminars. Including this one at QUT. Enjoy, if you happen to be down-under!