Posts Tagged ‘ Simulation ’

The foundations of Statistics [reply]

July 18, 2011
By
The foundations of Statistics [reply]

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

Read more »

The foundations of Statistics: a simulation-based approach

July 11, 2011
By
The foundations of Statistics: a simulation-based approach

“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

Read more »

Hammersley and Handscomb 1964 on line

May 26, 2011
By
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

Read more »

No simulation is complete without a gif

March 24, 2011
By
No simulation is complete without a gif

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 →

Read more »

Bertand’s paradox [R details]

March 19, 2011
By
Bertand’s paradox [R details]

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

Read more »

Plotting Indifference Curves with R Contour Function

March 11, 2011
By
Plotting Indifference Curves with R Contour Function

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...

Read more »

Copula Functions, R, and the Financial Crisis

March 10, 2011
By
Copula Functions, R, and the Financial Crisis

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...

Read more »

Adap’skiii [day 2]

January 5, 2011
By
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

Read more »

History makes Stat. Science!

December 31, 2010
By
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

Read more »

More typos in Chapter 5

December 29, 2010
By
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

Read more »