Posts Tagged ‘ University life ’

weakly informative reparameterisations for location-scale mixtures

January 18, 2017
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weakly informative reparameterisations for location-scale mixtures

We have been working towards a revision of our reparameterisation paper for quite a while now and too advantage of Kate Lee visiting Paris this fortnight to make a final round: we have now arXived (and submitted) the new version. The major change against the earlier version is the extension of the approach to a […]

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truncated normal algorithms

January 3, 2017
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truncated normal algorithms

Nicolas Chopin (CREST) just posted an entry on Statisfaction about the comparison of truncated Normal algorithms run by Alan Rogers, from the University of Utah. Nicolas wrote a paper in Statistics and Computing about a simulation method, which proposes a Ziggurat type of algorithm for this purpose, and which I do not remember reading, thanks […]

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ratio-of-uniforms [-1]

December 11, 2016
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ratio-of-uniforms [-1]

Luca Martino pointed out to me my own and forgotten review of a 2012 paper of his, “On the Generalized Ratio of Uniforms as a Combination of Transformed Rejection and Extended Inverse of Density Sampling” that obviously discusses a generalised version of Kinderman and Monahan’s (1977) ratio-of-uniform method. And further points out the earlier 1991 paper […]

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ratio-of-uniforms [#4]

December 1, 2016
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ratio-of-uniforms [#4]

Possibly the last post on random number generation by Kinderman and Monahan’s (1977) ratio-of-uniform method. After fiddling with the Gamma(a,1) distribution when a<1 for a while, I indeed figured out a way to produce a bounded set with this method: considering an arbitrary cdf Φ with corresponding pdf φ, the uniform distribution on the set […]

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Monty Python generator

November 22, 2016
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Monty Python generator

By some piece of luck I came across a paper by the late George Marsaglia, genial contributor to the field of simulation, and Wai Wan Tang, entitled The Monty Python method for generating random variables. As shown by the below illustration, the concept is to flip the piece H outside the rectangle back inside the […]

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postdoc on missing data at École Polytechnique

November 17, 2016
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postdoc on missing data at École Polytechnique

Julie Josse contacted me for advertising a postdoc position at École Polytechnique, in Palaiseau, south of Paris. “The fellowship is focusing on missing data. Interested graduates should apply as early as possible since the position will be filled when a suitable candidate is found. The Centre for Applied Mathematics (CMAP) is  looking for highly motivated […]

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simulation under zero measure constraints

November 16, 2016
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simulation under zero measure constraints

A theme that comes up fairly regularly on X validated is the production of a sample with given moments, either for calibration motives or from a misunderstanding of the difference between a distribution mean and a sample average. Here are some entries on that topic: How to sample from a distribution so that mean of […]

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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 [I mean, real, recognised, publishing, senior 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 [from 2013] 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 [toy] joint Beta x Binomial target, which leads to a basic Gibbs sampler. We thought this was […]

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