Blog Archives

Adap’skiii [day 2]

January 5, 2011
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Adap’skiii [day 2]

Another exciting day at Adap’skiii!!! Yves Atchadé presented a very recent work on the fundamental issue of estimating the asymptotic variance estimation for adaptive MCMC algorithms, with an intriguing experimental observation that a non-converging bandwidth with rate 1/n was providing better coverage than the converging rate. (I always found the issue of estimating the asymptotic

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Le Monde puzzle [52|solution]

January 1, 2011
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Le Monde puzzle [52|solution]

I have now received the first issue of Le Monde magazine, including the solution to puzzle #52 I solved just in time by simulated annealing! The trick is in using the following theorem: Iter(1,x,y) is divisible by 10x-1 if and only if y is divisible by 10x-1. Then the value to be found is divisible

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History makes Stat. Science!

December 31, 2010
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History makes Stat. Science!

While the above heading sounds like a title in reverse, its words are in the “correct” order in that our paper with George Casella, A Short History of Markov Chain Monte Carlo, has been accepted for publication by Statistical Science. This publication may sound weird when considering that the paper is also scheduled to appear

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Le Monde puzzle [52]

December 31, 2010
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Le Monde puzzle [52]

The last puzzle of the year in Le Monde reads as follows (as far as I understand its wording!): Iter(n,x,y) is the function Iter=function(n,x,y){ if (n==1){ output=trunc(y/10)+x*(y%%10) }else{ output=Iter(n-1,x,Iter(1,x,y))} return output } Find the seven-digit number z such that Iter(6,1,z)=12, Iter(6,2,z)=19, Iter(6,3,z)=29, and Iter(6,-1,z)=Iter(6,-2,z)=Iter(6,-3,z)=0. Obviously, the brute-force solution of listing all 90 million seven digit

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More typos in Chapter 5

December 29, 2010
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More typos in Chapter 5

Following Ashley’s latest comments on Chapter 5 of Introducing Monte Carlo Methods with R, I realised Example 5.5 was totally off-the-mark! Not only the representation of the likelihood should have used prod instead of mean, not only the constant should call the val argument of integrate, not only integrate  uses lower and upper rather than

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nlm [unused argument(s) (iter = 1)]

December 28, 2010
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nlm [unused argument(s) (iter = 1)]

Ashley put the following comment on Chapter 5 of Introducing Monte Carlo Methods with R”: I am reading chapter 5. I try to reproduced the result on page 128. The R codes don’t work on my laptop. When I try to run the following codes on page 128 > for (i in 1:(nlm(like,sta)$it)){ + mmu=rbind(mmu,nlm(like,sta,iter=i)$est)}

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Poster at MCMSki III

December 28, 2010
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Poster at MCMSki III

Here is the poster presented at MCMSki III next week by Pierre Jacob about our joint paper on parallelisation: Filed under: R, Statistics, Travel Tagged: Adapski, MCMC, MCMSki, Metropolis-Hastings, Monte Carlo Statistical Methods, parallelisation, pos...

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Questions on the parallel Rao-Blackwellisation

December 21, 2010
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Questions on the parallel Rao-Blackwellisation

Pierre Jacob and I got this email from a student about our parallel Rao-Blackwellisation paper. Here are some parts of the questions and our answer: Although I understand how the strategy proposed in the paper helps in variance reduction, I do not understand why you set b=1 (mentioned in Section 3.2) and why it plays

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Back from Philly

December 20, 2010
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Back from Philly

The conference in honour of Larry Brown was quite exciting, with lots of old friends gathered in Philadelphia and lots of great talks either recollecting major works of Larry and coauthors or presenting fairly interesting new works. Unsurprisingly, a large chunk of the talks was about admissibility and minimaxity, with John Hartigan starting the day

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AMIS revised & resubmitted

December 18, 2010
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AMIS revised & resubmitted

After a thorough revision that removed most of the theoretical attempts at improving our understanding of AMIS convergence, we have now resubmitted the AMIS paper to Scandinavian Journal of Statistics and arXived the new version as well. (I remind the reader that AMIS stands for adaptive mixture importance sampling and that it implements an adaptive

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