Posts Tagged ‘ University life ’

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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variance of an exponential order statistics

November 9, 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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SAS on Bayes

November 7, 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 [Bayesian inference] provides inferences that are conditional on the data and are exact, […]

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an attempt at EP-ABC from scratch, nothing more… [except for a few bugs]

October 18, 2016
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an attempt at EP-ABC from scratch, nothing more… [except for a few bugs]

Following a request from one of the reviewers of our chapter Likelihood-free model choice, I tried to run EP-ABC on a toy problem and to compare it with the outcome of a random forest ABC. Literally starting from scratch, namely from the description found in Simon and Nicolas’ JASA paper.  To run my test, I […]

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