Posts Tagged ‘ Monte Carlo Statistical Methods ’

slides for my simulation course

October 18, 2012
By
slides for my simulation course

Similar to last year, I am giving a series of lectures on simulation jointly as a Master course in Paris-Dauphine and as a 3rd year course in ENSAE. The course borrows from both the books Monte Carlo Statistical Methods and from Introduction to Monte Carlo Methods with R, with George Casella. Here are the three

Read more »

Course at Monash (#2)

July 19, 2012
By
Course at Monash (#2)

Here are the slides for the second day of my course at Monash University, Melbourne, in the Special Lectures in Econometrics, with a strong strong similarity with the slides of my course in Roma this Spring. (Ah, sunny Roma…) The first day lecture was very well attended and I hope this remains true for the

Read more »

Course at Monash (#1)

July 18, 2012
By
Course at Monash (#1)

Here are the slides for the first day of my course at Monash University, Melbourne, in the Special Lectures in Econometrics, with a strong similarity with the slides of my course in Wharton, two years ago. (Be sure to check slide 67! If the update on slideshare works from my flat in Melbourne…) Filed under:

Read more »

\STATE [algorithmic package]

June 7, 2012
By
\STATE [algorithmic package]

I fought with my LαTεX compiler this morning as it did not want to deal with my code: looking on forums for incompatibilities between beamer and algorithmic, and adding all kinds of packages, to no avail. Until I realised one \STATE was missing: (This is connected with my AMSI public lecture on simulation, obviously!) Filed

Read more »

ABC+EL=no D(ata)

May 27, 2012
By
ABC+EL=no D(ata)

It took us a loooong while but we finally ended up completing a paper on ABC using empirical likelihood (EL) that was started by me listening to Brunero Liseo’s tutorial in O’Bayes-2011 in Shanghai… Brunero mentioned empirical likelihood as a semi-parametric technique w/o much Bayesian connections and this got me thinking

Read more »

IS vs. self-normalised IS

March 11, 2012
By
IS vs. self-normalised IS

I was grading my Master projects this morning and came upon this graph: which compares the variability of an importance-sampling estimator versus its self-normalised alternative… This is an interesting case in that self-normalisation does considerably degrade the quality of the approximation in that setting. In other cases, self-normalisation may bring a clear improvement. (This reminded

Read more »

another X’idated question

February 23, 2012
By
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,

Read more »

recents advances in Monte Carlo Methods

February 8, 2012
By
recents advances in Monte Carlo Methods

Next Thursday (Jan. 16), at the RSS, there will be a special half-day meeting (afternoon, starting at 13:30) on Recent Advances in Monte Carlo Methods organised by the General Application Section. The speakers are Richard Everitt, University of Oxford, Missing data, and what to do about it Anthony Lee, Warwick University, Auxiliary variables and many-core

Read more »

Andrew gone NUTS!

November 23, 2011
By
Andrew gone NUTS!

Matthew Hoffman and Andrew Gelman have posted a paper on arXiv entitled “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo” and developing an improvement on the Hamiltonian Monte Carlo algorithm called NUTS (!). Here is the abstract: Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the

Read more »

understanding computational Bayesian statistics: a reply from Bill Bolstad

October 23, 2011
By
understanding computational Bayesian statistics: a reply from Bill Bolstad

Bill Bolstad wrote a reply to my review of his book Understanding computational Bayesian statistics last week and here it is, unedited except for the first paragraph where he thanks me for the opportunity to respond, “so readers will see that the book has some good features beyond having a “nice cover”.” (!) I simply processed

Read more »