Blog Archives

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 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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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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Graphical comparison of MCMC performance [arXiv:1011.445]

November 22, 2010
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Graphical comparison of MCMC performance [arXiv:1011.445]

A new posting on arXiv by Madeleine Thompson on a graphical tool for assessing performance. She has developed a software called SamplerCompare, implemented in R and C. The graphical evaluation plots “log density evaluations per iteration times autocorrelation time against a tuning parameter in a grid of plots where rows represent distributions and columns represent

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Introducing Monte Carlo in PaRis [more slides]

November 17, 2010
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Introducing Monte Carlo in PaRis [more slides]

The class started yesterday with a small but focussed and responsive audience! Given the background of the students, and in particular their clear proficiency in R!, I switched between the original slides of Introducing Monte Carlo Methods with R and those of my Monte Carlo Statistical Methods: course, updated by Olivier Cappé who is teaching

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Postdoc in Wharton

November 16, 2010
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Postdoc in Wharton

Just received this email from José Bernardo about an exciting postdoc position in Wharton: POST-DOCTORAL FELLOW – DEPARTMENT OF STATISTICS, THE WHARTON SCHOOL The Department of Statistics at The Wharton School of the University of Pennsylvania is seeking candidates for a Post-Doctoral Fellowship. This research fellowship provides full funding without any teaching requirements at a

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Introducing Monte Carlo in PaRis

November 14, 2010
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Introducing Monte Carlo in PaRis

As already announced on Statisfaction, I will start a short course in English based on Introducing Monte Carlo Methods with R at ENSAE next Tuesday. The slides were written by George Casella for a course he gave in Italy last spring and he kindly agreed on making them available on slideshare: Filed under:

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Bayesian Inference for Latent Gaussian Models

November 12, 2010
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Bayesian Inference for Latent Gaussian Models

An exciting conference in Zurich next February, 02-05. (I think I will attend! And not for skiing reasons!) Latent Gaussian models have numerous applications, for example in spatial and spatio-temporal epidemiology and climate modelling. This workshop brings together researchers who develop and apply Bayesian inference in this broad model class. One methodological focus is on

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