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analysing the US election result, from Oxford, England

November 14, 2016
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analysing the US election result, from Oxford, England

Seth Flaxman (Oxford), Dougal J. Sutherland (UCL), Yu-Xiang Wang (CMU), and Yee Whye Teh (Oxford), published on arXiv this morning an analysis of the US election, in what they called most appropriately a post-mortem. Using ecological inference already employed after Obama’s re-election. And producing graphs like the following one:Filed under: pictures, R, Statistics, Travel, University

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copy code at your own peril

November 13, 2016
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copy code at your own peril

I have come several times upon cases of scientists from other fields blindly copying MCMC code from a paper or website, and expecting the program to operate on their own problem… One illustration is from last week, when I read a X Validated question about an

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Example 7.3: what a mess!

November 12, 2016
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Example 7.3: what a mess!

A rather obscure question on Metropolis-Hastings algorithms on X Validated ended up being about our first illustration in Introducing Monte Carlo methods with R. And exposing some inconsistencies in the following example… Example 7.2 is based on a joint Beta x Binomial target, which leads to a basic Gibbs sampler. We thought this was

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variance of an exponential order statistics

November 7, 2016
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variance of an exponential order statistics

This afternoon, one of my Monte Carlo students at ENSAE came to me with an exercise from Monte Carlo Statistical Methods that I did not remember having written. And I thus “charged” George Casella with authorship for that exercise! Exercise 3.3 starts with the usual question (a) about the (Binomial) precision of a tail probability

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ratio-of-uniforms [#3]

November 3, 2016
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ratio-of-uniforms [#3]

Being still puzzled (!) by the ratio-of-uniform approach, mostly failing to catch its relevance for either standard distributions in a era when computing a cosine or an exponential is negligible, or non-standard distributions for which computing bounds and boundaries is out-of-reach, I kept searching for solutions that would include unbounded densities and still produce compact

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je reviendrai à Montréal [MCM 2017]

November 2, 2016
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je reviendrai à Montréal [MCM 2017]

Next summer of 2017, the biennial International Conference on Monte Carlo Methods and Applications (MCM) will take place in Montréal, Québec, Canada, on July 3-7. This is a mathematically-oriented meeting that works in alternance with MCqMC and that is “devoted to the study of stochastic simulation and Monte Carlo methods in general, from the theoretical

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SAS on Bayes

November 1, 2016
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SAS on Bayes

Following a question on X Validated, I became aware of the following descriptions of the pros and cons of Bayesian analysis, as perceived by whoever (Tim Arnold?) wrote SAS/STAT(R) 9.2 User’s Guide, Second Edition. I replied more specifically on the point It provides inferences that are conditional on the data and are exact,

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ratio-of-uniforms [#2]

October 30, 2016
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ratio-of-uniforms [#2]

Following my earlier post on Kinderman’s and Monahan’s (1977) ratio-of-uniform method, I must confess I remain quite puzzled by the approach. Or rather by its consequences. When looking at the set A of (u,v)’s in R⁺×X such that 0≤u²≤ƒ(v/u), as discussed in the previous post, it can be represented by its parameterised boundary u(x)=√ƒ(x),v(x)=x√ƒ(x)    x

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a grim knight [cont’d]

October 19, 2016
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a grim knight [cont’d]

As discussed in the previous entry, there are two interpretations to this question from The Riddler: “…how long is the longest path a knight can travel on a standard 8-by-8 board without letting the path intersect itself?” as to what constitutes a path. As a (terrible) chess player, I would opt for the version on

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ratio-of-uniforms

October 19, 2016
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ratio-of-uniforms

One approach to random number generation that had always intrigued me is Kinderman and Monahan’s (1977) ratio-of-uniform method. The method is based on the result that the uniform distribution on the set A of (u,v)’s in R⁺xX such that 0≤u²≤ƒ(v/u) induces the distribution with density proportional to ƒ on V/U. Hence the name. The proof

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