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Random variable generation (Pt 1 of 3)

November 28, 2010
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Random variable generation (Pt 1 of 3)

As I mentioned in a recent post, I’ve just received a copy of Advanced Markov Chain Monte Carlo Methods. Chapter 1.4 in the book (very quickly) covers random variable generation. Inverse CDF Method A standard algorithm for generating random numbers is the inverse cdf method. The continuous version of the algorithm is as follows: 1.

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Computational efficiency of great-circle distance calculations in R

November 28, 2010
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Computational efficiency of great-circle distance calculations in R

An obvious omission in my previous post on Great-circle distance calculations in R was a lack of discussion on the computational efficiency of the various methods, and in particular comparing different implementations of the same method. One of the comments … Continue reading →

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Advanced Markov Chain Monte Carlo Methods (AMCMC)

November 27, 2010
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Advanced Markov Chain Monte Carlo Methods (AMCMC)

I’ve just received my copy of Advanced Markov Chain Monte Carlo Methods, by Liang, Liu, & Carroll. Although my PhD didn’t really involve any Bayesian methodology (and my undergrad was devoid of any Bayesian influence), I’ve found that the sort of problems I’m now tackling in systems biology demand a Bayesian/MCMC approach. There are a

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Random graphs with fixed numbers of neighbours

November 24, 2010
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Random graphs with fixed numbers of neighbours

In connection with Le Monde puzzle #46, I eventually managed to write an R program that generates graphs with a given number n of nodes and a given number k of edges leaving each of those nodes. (My early attempt was simply too myopic to achieve any level of success when n was larger than

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The joys of teaching R

November 23, 2010
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The joys of teaching R

Just read a funny but much to the point blog entry on the difficulties of teaching proper programming skills to first year students! I will certainly make use of the style file as grading 180 exams is indeed a recurrent nightmare… Filed under: R,...

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Great-circle distance calculations in R

November 23, 2010
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Great-circle distance calculations in R

Recently I found myself needing to calculate the distance between a large number of longitude and latitude locations. As it turns out, because the earth is a three-dimensional object, you cannot simply pretend that you are in Flatland, albeit some … Continue reading →

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R and AOL in NYC

November 23, 2010
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R and the NYC R User Group get brief mentions in this article about AOL's offices in New York City. The NYC UseRs meet at AOL and (ironically) the next meeting on Dec 9 is on the topic of R at Google. New York Observer: Bringing Some Sizzle to the Dial-Up King (via)

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R Style Guide

November 23, 2010
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R Style Guide

Each year I have the pleasure (actually it’s quite fun) of teaching R programming to first year mathematics and statistics students. The vast majority of these students have no experience of programming, yet think they are good with computers because they use facebook! The class has around 100 students, and there are eight practicals. In

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Robust adaptive Metropolis algorithm [arXiv:10114381]

November 23, 2010
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Robust adaptive Metropolis algorithm [arXiv:10114381]

Matti Vihola has posted a new paper on arXiv about adaptive (random walk) Metropolis-Hastings algorithms. The update in the (lower diagonal) scale matrix is where is the current acceptance probability and the target acceptance rate; is the current random noise for the proposal, ; is a step size sequence decaying to zero. The spirit of

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R.I.P. StatProb?

November 22, 2010
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R.I.P. StatProb?

As posted in early August from JSM 2010 in Vancouver, StatProb was launched as a way to promote an on-line encyclopedia/wiki with the scientific backup of expert reviewers. This was completely novel and I was quite excited to take part in the venture as a representative of the Royal Statistical Society. Most unfortunately, the separation

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