Publications by xi'an
MCMSki IV, Jan. 6-8, 2014, Chamonix (news #10)
This a final reminder about the October 15 deadlines for MCMSki IV: First, the early bird rate for the registration ends up on October 15. Second, the young investigator travel support can only be requested up to October 15 as well. (For those waiting for the decision about the support to register, the registration deadline will be lifted!) Thi...
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accelerated ABC
On the flight back from Warwick, I read a fairly recently arXived paper by Umberto Picchini and Julie Forman entitled “Accelerating inference for diffusions observed with measurement error and large sample sizes using Approximate Bayesian Computation: A case study” that relates to earlier ABC works (and the MATLAB abc-sde package) by the firs...
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beta HPD
While writing an introductory chapter on Bayesian analysis (in French), I came by the issue of computing an HPD region when the posterior distribution is a Beta B(α,β) distribution… There is no analytic solution and hence I resorted to numerical resolution (provided here for α=117.5, β=115.5): f=function(p){ # find the symmetric g=func...
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drawing surface plots on the IR³ simplex
As a result of a corridor conversation in Warwick, I started looking at distributions on the IR³ simplex, and wanted to plot the density in a nice way. As I could not find a proper package on CRAN, the closer being the BMAmevt (for Bayesian Model Averaging for Multivariate Extremes) R package developed by a former TSI Master student, Anne Sabou...
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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 for Warwick. (And in the train to Argentan.) It...
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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 the ap...
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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 was unusual. And reported one mass mur...
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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 Related To leave a comment for the author, please follow the link and comment on their blog: Xi...
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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 bisquare numbers) and (ii) both xy and (x+y) ar...
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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 ra...
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