Blog Archives

Advances in scalable Bayesian computation [day #4]

March 7, 2014
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Advances in scalable Bayesian computation [day #4]

Final day of our workshop Advances in Scalable Bayesian Computation already, since tomorrow morning is an open research time ½ day! Another “perfect day in paradise”, with the Banff Centre campus covered by a fine snow blanket, still falling…, and making work in an office of BIRS a dream-like moment. Still looking for a daily theme,

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Advances in scalable Bayesian computation [day #3]

March 6, 2014
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Advances in scalable Bayesian computation [day #3]

We have now gone over the midpoint of our workshop Advances in Scalable Bayesian Computation with three talks in the morning and an open research or open air afternoon. (Maybe surprisingly I chose to stay indoors and work on a new research topic rather than trying cross-country skiing!) If I must give a theme for

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Advances in scalable Bayesian computation [day #2]

March 5, 2014
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Advances in scalable Bayesian computation [day #2]

And here is the second day of our workshop Advances in Scalable Bayesian Computation gone! This time, it sounded like the “main” theme was about brains… In fact, Simon Barthelmé‘s research originated from neurosciences, while Dawn Woodard dissected a brain (via MRI) during her talk! (Note that the BIRS website currently posts Simon’s video as

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Advances in scalable Bayesian computation [day #1]

March 4, 2014
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Advances in scalable Bayesian computation [day #1]

This was the first day of our workshop Advances in Scalable Bayesian Computation and it sounded like the “main” theme was probabilistic programming, in tune with my book review posted this morning. Indeed, both Vikash Mansinghka and Frank Wood gave talks about this concept, Vikash detailing the specifics of a new programming language called Venture

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Foundations of Statistical Algorithms [book review]

February 27, 2014
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Foundations of Statistical Algorithms [book review]

There is computational statistics and there is statistical computing. And then there is statistical algorithmic. Not the same thing, by far. This 2014 book by Weihs, Mersman and Ligges, from TU Dortmund, the later being also a member of the R Core team, stands at one end of this wide spectrum of techniques required by

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Nonlinear Time Series just appeared

February 25, 2014
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Nonlinear Time Series just appeared

My friends Randal Douc and Éric Moulines just published this new time series book with David Stoffer. (David also wrote Time Series Analysis and its Applications with Robert Shumway a year ago.) The books reflects well on the research of Randal and Éric over the past decade, namely convergence results on Markov chains for validating

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evaluating stochastic algorithms

February 19, 2014
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evaluating stochastic algorithms

Reinaldo sent me this email a long while ago Could you recommend me a nice reference about measures to evaluate stochastic algorithms (in particular focus in approximating posterior distributions). and I hope he is still reading the ‘Og, despite my lack of prompt reply! I procrastinated and procrastinated in answering this question as I did not

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Statistics and Computing special MCMSk’issue [call for papers]

February 6, 2014
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Statistics and Computing special MCMSk’issue [call for papers]

Following the exciting and innovative talks, posters and discussions at MCMski IV, the editor of Statistics and Computing, Mark Girolami (who also happens to be the new president of the BayesComp section of ISBA, which is taking over the management of future MCMski meetings), kindly proposed to publish a special issue of the journal open

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cut, baby, cut!

January 28, 2014
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cut, baby, cut!

At MCMSki IV, I attended (and chaired) a session where Martyn Plummer presented some developments on cut models. As I was not sure I had gotten the idea [although this happened to be one of those few sessions where

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Statistical modeling and computation [book review]

January 21, 2014
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Statistical modeling and computation [book review]

Dirk Kroese (from UQ, Brisbane) and Joshua Chen (from ANU, Canberra) just published a book entitled Statistical Modeling and Computation, distributed by Springer-Verlag (I cannot tell which series it is part of from the cover or frontpages…) The book is intended mostly for an undergrad audience (or for graduate students with no probability or statistics

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