Blog Archives

Importance sampling schemes for evidence approximation in mixture models

November 26, 2013
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Importance sampling schemes for evidence approximation in mixture models

Jeong Eun (Kate) Lee and I completed this paper, “Importance sampling schemes for evidence approximation in mixture models“, now posted on arXiv. (With the customary one-day lag for posting, making me bemoan the days of yore when arXiv would give a definitive arXiv number at the time of submission.) Kate came twice to Paris in the past

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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #12)

November 26, 2013
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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #12)

We are converging towards MCMSki IV getting closer and closer to the conference! I hope that by now all intended participants have registered (registration is still open!), found a place where to stay during and around the conference (still feasible!), and booked their flight to Geneva (or nearby). First, please send me asap the  poster abstract

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Buffon needled R exams

November 24, 2013
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Buffon needled R exams

Here are two exercises I wrote for my R mid-term exam in Paris-Dauphine around Buffon’s needle problem. In the end, the problems sounded too long and too hard for my 3rd year students so I opted for softer questions. So recycle those if you wish (but do not ask for solutions!) Filed under: Books, Kids,

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Le Monde puzzle [#840]

November 22, 2013
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Le Monde puzzle [#840]

Another number theory Le Monde mathematical puzzles: Find 2≤n≤50 such that the sequence {1,…,n} can be permuted into a sequence such that the sum of two consecutive terms is a prime number.  Now this is a problem with an R code solution: which returns the solution as and so it seems there is no solution beyond N=12…

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On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

November 19, 2013
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On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

Perrakis, Ntzoufras, and Tsionas just arXived a paper on marginal likelihood (evidence) approximation (with the above title). The idea behind the paper is to base importance sampling for the evidence on simulations from the product of the (block) marginal posterior distributions. Those simulations can be directly derived from an MCMC output by randomly permuting the

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Le Monde puzzle [#839]

November 15, 2013
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Le Monde puzzle [#839]

A number theory Le Monde mathematical puzzle whose R coding is not really worth it (and which rings a bell of a similar puzzle in the past, puzzle I cannot trace…): The set Ξ is made of pairs of integers (x,y) such that (i) both x and y are written as a sum of two squared integers (i.e., are

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Bayesian essentials with R available on amazon

November 14, 2013
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Bayesian essentials with R available on amazon

Bayesian Essentials with R is now available both as an e-book and as a hardcover book on amazon.com!Filed under: Books, R, Statistics, University life Tagged: Bayesian Core, Bayesian Essentials with R, e-book, Jean-Michel Marin, R, Springer-Verlag

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Unusual timing shows how random mass murder can be (or not)

November 3, 2013
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Unusual timing shows how random mass murder can be (or not)

This was one headline in the USA Today I picked from the hotel lobby on my way to Pittsburgh airport and then Toronto this morning. The unusual pattern was about observing four U.S. mass murders happening within four days, “for the first time in at least seven years”. The article did not explain why this

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machine learning [book review, part 2]

October 21, 2013
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machine learning [book review, part 2]

The chapter (Chap. 3) on Bayesian updating or learning (a most appropriate term) for discrete data is well-done in Machine Learning, a probabilistic perspective if a bit stretched (which is easy with 1000 pages left!). I like the remark (Section 3.5.3) about the log-sum-exp trick. While lengthy, the chapter (Chap. 4) on Gaussian models has

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machine learning [book review]

October 20, 2013
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machine learning [book review]

I have to admit the rather embarrassing fact that Machine Learning, A probabilistic perspective by Kevin P. Murphy is the first machine learning book I really read in detail…! It is a massive book with close to 1,100 pages and I thus hesitated taking it with me around, until I grabbed it in my bag

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