Publications by xi'an
10w2170, Banff [2]
Over the two days of the Hierarchical Bayesian Methods in Ecology workshop, we managed to cover normal models, testing, regression, Gibbs sampling, generalised linear models, Metropolis-Hastings algorithms and of course a fair dose of hierarchical modelling. At the end of the Saturday marathon session, we spent one and half discussing some models...
3693 sym 18 img
News on MCMSki III
Here is a message sent by the organisers of MCMSki III in Utah next early January. When registering, make sure to tick the free registration for Adap’skiii as well! The fourth joint international meeting of the IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), nicknamed “MCMSki III“, will be ...
3384 sym 18 img
Typo in Example 3.6
Edward Kao pointed out the following difficulty about Example 3.6 in Chapter 3 of “Introducing Monte Carlo Methods with R”: I have two questions that have puzzled me for a while. I hope you can shed some lights. They are all about Example 3.6 of your book. 1. On page 74, there is a term x(1-x) for m(x). This is fine. But the term disappe...
1700 sym 18 img
R tee-shirt
I gave my introduction to the R course in a crammed amphitheatre of about 200 students today. Had to wear my collectoR teeshirt from Revolution Analytics, even though it only made the kids pay attention for about 30 seconds… The other few “lines” that worked were using the Proctor & Gamble “car 54″ poster and calling bootstrap “Statis...
1049 sym 20 img
Monte Carlo Statistical Methods third edition
Last week, George Casella and I worked around the clock on starting the third edition of Monte Carlo Statistical Methods by detailing the changes to make and designing the new table of contents. The new edition will not see a revolution in the presentation of the material but rather a more mature perspective on what matters most in statistical si...
4882 sym 18 img
Effective sample size
In the previous days I have received several emails asking for clarification of the effective sample size derivation in “Introducing Monte Carlo Methods with R” (Section 4.4, pp. 98-100). Formula (4.3) gives the Monte Carlo estimate of the variance of a self-normalised importance sampling estimator (note the change from the original version i...
1358 sym 36 img
Riemann, Langevin & Hamilton [reply]
Here is a (prompt!) reply from Mark Girolami corresponding to the earlier post: In preparation for the Read Paper session next month at the RSS, our research group at CREST has collectively read the Girolami and Calderhead paper on Riemann manifold Langevin and Hamiltonian Monte Carlo methods and I hope we will again produce a joint arXiv preprin...
8997 sym 30 img
Galton & simulation
Stephen Stigler has written a paper in the Journal of the Royal Statistical Society Series A on Francis Galton’s analysis of (his cousin) Charles Darwin’ Origin of Species, leading to nothing less than Bayesian analysis and accept-reject algorithms! “On September 10th, 1885, Francis Galton ushered in a new era of Statistical Enlightenment...
3805 sym 30 img
Le Monde puzzle [38]
Since I have resumed my R class, I will restart my resolution of Le Monde mathematical puzzles…as they make good exercises for the class. The puzzle this week is not that exciting: Find the four non-zero different digits a,b,c,d such that abcd is equal to the sum of all two digit numbers made by picking without replacement two digits from {a,b...
1126 sym R (174 sym/1 pcs) 16 img
Typo in Example 5.18
Edward Kao pointed out several typos in Example 5.18 of Monte Carlo Statistical Methods. First, the customers in area i should be double-indexed, i.e. which implies in turn that . Then the summary T should be defined as and as given that the first m customers have the fifth plan missing. Filed under: Books, R, Statistics, University life Tagg...
823 sym 28 img