# Posts Tagged ‘ Simulation ’

## The foundations of Statistics [reply]

July 18, 2011
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Shravan Vasishth has written a response to my review both published on the Statistics Forum. His response is quite straightforward and honest. In particular, he acknowledges not being a statistician and that he “should spend more time studying statistics”. I also understand the authors’ frustration at trying “to recruit several statisticians (at different points) to

## The foundations of Statistics: a simulation-based approach

July 11, 2011
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“We have seen that a perfect correlation is perfectly linear, so an imperfect correlation will be `imperfectly linear’.” page 128 This book has been written by two linguists, Shravan Vasishth and Michael Broe, in order to teach statistics “in  areas that are traditionally not mathematically demanding” at a deeper level than traditional textbooks “without using

## Hammersley and Handscomb 1964 on line

May 26, 2011
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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

## No simulation is complete without a gif

March 24, 2011
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I promise this is my last post on the now week and a half old π pay! Building on the last post, I figured I could show how convergence actually works in the estimation algorithm. If you’ll recall, we plotted … Continue reading →

March 19, 2011
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Some may have had reservations about the “randomness” of the straws I plotted to illustrate Bertrand’s paradox. As they were all going North-West/South-East. I had actually made an inversion between cbind and rbind in the R code, which explained for this non-random orientation. Above is the corrected version, which sounds “more random” indeed. (And using

## Plotting Indifference Curves with R Contour Function

March 11, 2011
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The following post at Constructing Difference Curves - Part 3 from economics.about.com provides a discussion on indifference curves (but actually I think they are isoquants) and how to construct them. I think I have a grasp on how to do this in R if yo...

## Copula Functions, R, and the Financial Crisis

March 10, 2011
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From: In defense of the Gaussian copula, The Economist"The Gaussian copula provided a convenient way to describe a relationship that held under particular conditions. But it was fed data that reflected a period when housing prices were not correlated to the extent that they turned out to be when the housing bubble popped."Decisions about...

January 5, 2011
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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

## History makes Stat. Science!

December 31, 2010
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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

## More typos in Chapter 5

December 29, 2010
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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