Posts Tagged ‘ Monte Carlo methods ’

simulation, an ubiquitous tool

July 10, 2012
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simulation, an ubiquitous tool

(This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers) After struggling for quite a walk on that AMSI public lecture talk, and dreading its loss with the problematic Macbook, I managed to complete a first draft last night in Adelaide, downloading a final set of images from the Web...

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another X’idated question

February 23, 2012
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another X’idated question

An X’idated reader of Monte Carlo Statistical Methods had trouble with our Example 3.13, the very one our academic book reviewer disliked so much as to “diverse a 2 star”. The issue is with computing the integral when f is the Student’s t(5) distribution density. In our book, we compare a few importance sampling solutions,

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ultimate R recursion

January 31, 2012
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ultimate R recursion

One of my students wrote the following code for his R exam, trying to do accept-reject simulation (of a Rayleigh distribution) and constant approximation at the same time: which I find remarkable if alas doomed to fail! I wonder if there exists a (real as opposed to fantasy) computer language where you could introduce constants

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Hammersley and Handscomb 1964 on line

May 26, 2011
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Hammersley and Handscomb 1964 on line

Through the webpage of the Advanced Monte Carlo Methods I & II, given a few years ago by Michael Mascagni at ETH Zürich, I found a link to the scanned version of the 1964 book Monte Carlo Methods by Hammersley and Handscomb. This is a short book, with less than 150 pages, especially if one

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A survey of the [60′s] Monte Carlo methods [2]

May 17, 2011
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A survey of the [60′s] Monte Carlo methods [2]

The 24 questions asked by John Halton in the conclusion of his 1970 survey are Can we obtain a theory of convergence for random variables taking values in Fréchet spaces? Can the study of Monte Carlo estimates in separable Fréchet spaces give a theory of global approximation? When sampling functions, what constitutes a representative sample

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A survey of [the 60's] Monte Carlo methods

May 16, 2011
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A survey of [the 60's] Monte Carlo methods

“The only good Monte Carlos are the dead Monte Carlos” (Trotter and Tukey, quoted by Halton) When I presented my history of MCM methods in Bristol two months ago, at the Julian Besag memorial, Christophe Andrieu mentioned a 1970 SIAM survey by John Halton on A retrospective and prospective survey of the Monte Carlo

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ABC lectures [finale]

October 31, 2010
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ABC lectures [finale]

The latest version of my ABC slides is on slideshare. To conclude with a pun, I took advantage of the newspaper clipping generator once pointed out by Andrew. (Note that nothing written in the above should be taken seriously.) On the serious side, I managed to cover most of the 300 slides (!) over the

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Random generators for parallel processing

October 28, 2010
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Random generators for parallel processing

Given the growing interest in parallel processing through GPUs or multiple processors, there is a clear need for a proper use of (uniform) random number generators in this environment. We were discussing the issue yesterday with Jean-Michel Marin and briefly looked at a few solutions: given p parallel streams/threads/processors, starting each generator with a random

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NIPS 2010: Monte Carlo workshop

September 3, 2010
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NIPS 2010: Monte Carlo workshop

In the wake of the main machine learning NIPS 2010 meeting in Vancouver, Dec. 6-9 2010, there will be a very interesting workshop organised by Ryan Adams, Mark Girolami, and Iain Murray on Monte Carlo Methods for Bayesian Inference in Modern Day Applications, on Dec. 10. (And in Whistler, not Vancouver!) I wish I could

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Stratified sampling

June 9, 2010
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Stratified sampling

The recently arXived paper of Goldstein, Rinott and Scarsini studies the impact of refining a partition on the precision of a stratified maximising/integration Monte Carlo approach. Quite naturally, if the partition gets improved, simulating points in each set of the partition can only improve the quality of the approximation, whether the problem is in maximising

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