Blog Archives

more e’s [and R’s]

February 21, 2016
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more e’s [and R’s]

Alex Thiéry suggested debiasing the biased estimate of e by Rhee and Glynn truncated series method, so I tried the method to see how much of an improvement (if any!) this would bring. I first attempted to naïvely implement the raw formula of Rhee and Glynn with a (large) Poisson distribution on the stopping rule

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R colours

February 17, 2016
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R colours

As I am always fishing for colour names in R, I looked for an easy summary of those names and found this table on New Energy Research blog, which I reproduce here for future access.  Filed under: pictures, R Tagged: colors(), colours(), R

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Гнеде́нко and Forsythe [and e]

February 15, 2016
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Гнеде́нко and Forsythe [and e]

In the wake of my earlier post on the Monte Carlo estimation of e and e⁻¹, after a discussion with my colleague Murray Pollock (Warwick) Gnedenko’s solution, he pointed out another (early) Monte Carlo approximation called Forsythe’s method. That is detailed quite neatly in Luc Devroye’s bible, Non-uniform random variate generation (a free bible!). The

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new version of abcrf

February 12, 2016
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new version of abcrf

Version 1.1 of our R library abcrf version 1.1  is now available on CRAN.  Improvements against the earlier version are numerous and substantial. In particular,  calculations of the random forests have been parallelised and, for machines with multiple cores, the computing gain can be enormous.Filed under: R, Statistics, University life Tagged: ABC model choice, CRAN,

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The answer is e, what was the question?!

February 11, 2016
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The answer is e, what was the question?!

A rather exotic question on X validated: since π can be approximated by random sampling over a unit square, is there an equivalent for approximating e? This is an interesting question, as, indeed, why not focus on e rather than π after all?! But very quickly the very artificiality of the problem comes back to

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optimal simulation on a convex set

February 3, 2016
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optimal simulation on a convex set

This morning, we had a jam session at the maths department of Paris-Dauphine where a few researchers & colleagues of mine presented their field of research to the whole department. Very interesting despite or thanks to the variety of topics, with forays into the three-body problem(s) , mean fields for Nash equilibrium (or

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Le Monde puzzle [#947]

February 1, 2016
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Le Monde puzzle [#947]

Another boardgame in Le Monde mathematical puzzle : Given an 8×8 chequerboard,  consider placing 2×2 tiles over this chequerboard until (a) the entire surface is covered and (b) removing a single 2×2 tile exposes some of the original chequerboard. What is the maximal number of 2×2 tiles one can set according to this scheme? And for

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love-hate Metropolis algorithm

January 27, 2016
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love-hate Metropolis algorithm

Hyungsuk Tak, Xiao-Li Meng and David van Dyk just arXived a paper on a multiple choice proposal in Metropolis-Hastings algorithms towards dealing with multimodal targets. Called “A repulsive-attractive Metropolis algorithm for multimodality” . The proposal distribution includes a downward

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R typos

January 26, 2016
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R typos

At MCMskv, Alexander Ly (from Amsterdam) pointed out to me some R programming mistakes I made in the introduction to Metropolis-Hastings algorithms I wrote a few months ago for the Wiley on-line encyclopedia! While the outcome (Monte Carlo posterior) of the corrected version is moderately changed this is nonetheless embarrassing! The example (if not the

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high dimension Metropolis-Hastings algorithms

January 25, 2016
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high dimension Metropolis-Hastings algorithms

When discussing high dimension models with Ingmar Schüster Schuster the other day, we came across the following paradox with Metropolis-Hastings algorithms. If attempting to simulate from a multivariate standard normal distribution in a large dimension, when starting from the mode of the target, i.e., its mean γ, leaving the

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