2061 search results for "twitter"

Analysis of retractions in PubMed

November 30, 2010
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Analysis of retractions in PubMed

As so often happens these days, a brief post at FriendFeed got me thinking about data analysis. Entitled “So how many retractions are there every year, anyway?”, the post links to this article at Retraction Watch. It discusses ways to estimate the number of retractions and in particular, a recent article in the Journal of

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Slices and crumbs [arXiv:1011.4722]

November 29, 2010
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Slices and crumbs [arXiv:1011.4722]

An interesting note was arXived a few days ago by Madeleine Thompson and Radford Neal. Beside the nice touch of mixing crumbs and slices, the neat idea is to have multiple-try proposals for simulating within a slice and to decrease the dimension of the simulation space at each try. This dimension diminution is achieved via

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Joy of Stats coming soon

November 29, 2010
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Joy of Stats coming soon

The Joy of Stats really is a joy.  It will be shown on BBC4, apparently scheduled for December 7.  (That date comes from Hans Rosling on twitter, I haven’t found scheduling evidence at the BBC.) I saw its debut at the Royal Statistical Society on World Statistics Day. Here is a five minute excerpt: You … Continue reading...

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Computing evidence

November 28, 2010
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Computing evidence

The book Random effects and latent variable model selection, edited by David Dunson in 2008 as a Springer Lecture Note. contains several chapters dealing with evidence approximation in mixed effect models. (Incidentally, I would be interested in the story behind the  Lecture Note as I found no explanation in the backcover or in the preface.

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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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