**H**ere is the cover of the Japanese translation of our Introducing Monte Carlo methods with R book. A few year after the French translation. It actually appeared last year in August but I was not informed of this till a few weeks ago. The publisher is Maruzen, with an associated webpage if you want to order… Unless I am confused the translators are Hiro Ishida and Kazue Ishida; they deserve a major ありがとう ! And too bad George is no longer with us: this must have been the first translation of one of his books in Japanese..

## Archive for Introducing Monte Carlo Methods with R

## Ｒによるモンテカルロ法入門

Posted in Books, R, Statistics with tags George Casella, Introducing Monte Carlo Methods with R, Japanese translation on May 14, 2013 by xi'an## CHANCE: special issue on George Casella’s books

Posted in Books, R, Statistics, University life with tags CHANCE, George Casella, Introducing Monte Carlo Methods with R, Monte Carlo Statistical Methods, Sam Behesta, statistical inference, Theory of Point Estimation, Variance Components on February 10, 2013 by xi'an **T**he special issue of CHANCE on George Casella’s books has now appeared and it contains both my earlier post on George passing away and reviews of several of his books, as follows:

- Andrew Gelman on Introducing Monte Carlo Methods with R
- Bill Strawderman on Statistical Inference
- Jean-Louis Foulley on Variance Components
- Larry Wasserman on Theory of Point Estimation
- Xiao-Li Meng on Monte Carlo Statistical Methods

Although all of those books have appeared between twenty and five years ago, the reviews are definitely worth reading! *(Disclaimer: I am the editor of the Books Review section who contacted friends of George to write the reviews, as well as the co-author of two of those books!)* They bring in my *(therefore biased)* opinion a worthy evaluation of the depths and impacts of those major books, and they also reveal why George was a great teacher, bringing much into the classroom and to his students… *(Unless I am confused the whole series of reviews is available to all, and not only to CHANCE subscribers. Thanks, Sam!)*

## Example 7.17 in Introduction to Monte Carlo methods with R

Posted in Books, R, Statistics, University life with tags Introducing Monte Carlo Methods with R, mcsm on January 4, 2012 by xi'an**I** received the following email about ** Introducing Monte Carlo Methods with R** a few days ago:

Hallo Dr. Robert,

I am studying your fine book for myself. There´s a little problem in examples 7.17 and 8.1: in the R code a function “

gu” is used and a reference given to ex. 5.17, but I cann´t find there a definition of “gu“. (gu = log formula (5.15)?) Could you give me a hint?

from Elmar Kisslinger. Indeed, the *gu* function used in this analysis of the logit model is not available in the book, it is provided by

#function for MCMC gu=function(mu,i,beta,sigma){ sum((y[i,]*(beta*x[i,]+mu))-log(1+exp(beta*x[i,]+mu)))-0.5*mu^2/sigma^2 }

and is only available in the associated mcsm R package as part of the *randogit.R* code. (Incidentally, this is my 1500th post on the ‘Og! And this coincides with the 3000th comment…)

## Typos in Introduction to Monte Carlo Methods with R

Posted in Books, R, Statistics, University life with tags Introducing Monte Carlo Methods with R, Japan, Monte Carlo Statistical Methods, perplexity, R on October 13, 2011 by xi'an**T**he two translators of our book in Japanese, Kazue & Motohiro Ishida, contacted me about some R code mistakes in the book. The translation is nearly done and they checked every piece of code in the book, an endeavour for which I am very grateful! Here are the two issues they have noticed (after incorporating the typos signaled in the overall up-to-date summary):

**F**irst, in Example 4.4, I omitted some checkings and forgot about a minus sign, meaning Figure 4.4 (right) is wrong. *(The more frustrating since this example covers perplexity!)* The zeros must be controlled via code lines like

> wachd[wachd<10^(-10)]=10^(-10)

instead of the meaningless

wachd[apply(wachd,2,cumsum)<10^(-10)]=10^(-10)

and the addition of

> plex[plex>0]=0 > plech[plech>0]=0

after the definition of those two variables. (Because entropies are necessarily positive.) The most glaring omission is however the minus in

> plob=apply(exp(-plex),1,quantile,c(.025,.975)) > ploch=apply(exp(-plech),1,quantile,c(.025,.975))

which modifies Figure 4.4 in the following

**T**he second case is Example 7.3 where I forgot to account for the log-transform of the data, which should read (p.204):

> x=c(91,504,557,609,693,727,764,803,857,929,970,1043, + 1089,1195,1384,1713) > x=log(x)

and compounded my mistake by including log-transforms of the parameters that should not be there (pp.204-205)! So (for my simulations) the posterior means of θ and σ² are 6.62 and 0.661, respectively, leading to an estimate of σ of 0.802. There should be no log transform in Exercise 7.3 either.

**T**he same corrections apply to the French translation, most obviously…

## what’s wrong with package comment?!

Posted in Books, R, Statistics, University life with tags comment package, Introducing Monte Carlo Methods with R, LaTeX, R, verbatim on May 4, 2012 by xi'anIspent most of the Sunday afternoon trying to understand why definingdid not have the same effect as writing the line

until I found there is a clash due to the

commentpackage… The assuredly simple codeproduces an error message:

This is quite an inconvenience as I need to compile my solution manual for “” with the even-numbered exercises commented out or not depending on the version… (Leaving this package out and using the comment command within theIntroducing Monte Carlo Methods with Rverbatimpackage does not work either becauseeradoes not seem to be recognised as the end of a commented part…)## Share:

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