Posts Tagged ‘ Simulation ’

recents advances in Monte Carlo Methods

February 8, 2012
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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

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semi-automatic ABC

December 17, 2011
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semi-automatic ABC

The talk of Wednesday afternoon Ordinary Meeting of the Royal Statistical Society went on quite well, I think. I would have expected a few people (in general) and some specific people (in particular) but this being the last week of term the schedule was not the best of times. Paul Fearnhead gave the talk, insisting

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Andrew gone NUTS!

November 23, 2011
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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

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Simulation: Efficiency of mean with median Goal: Show the…

October 31, 2011
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Simulation: Efficiency of mean with median

Goal: Show the…

Simulation: Efficiency of mean with median Goal: Show the efficiency of the mean when compared with the median using a large simulation where both estimators are applied on a sample of U(0,1) uniformly distributed random numbers. Input: # Goal: Show...

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understanding computational Bayesian statistics: a reply from Bill Bolstad

October 23, 2011
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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

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Simulating data following a given covariance structure

October 12, 2011
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Every year there is at least a couple of occasions when I have to simulate multivariate data that follow a given covariance matrix. For example, let’s say that we want to create an example of the effect of collinearity when … Continue reading →

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understanding computational Bayesian statistics

October 9, 2011
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understanding computational Bayesian statistics

I have just finished reading this book by Bill Bolstad (University of Waikato, New Zealand) which a previous ‘Og post pointed out when it appeared, shortly after our Introducing Monte Carlo Methods with R. My family commented that the cover was nicer than those of my own books, which is true. Before I launch into

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workshop in Columbia [day 3]

September 26, 2011
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workshop in Columbia [day 3]

Although this was only a half-day of talks, the third day of the workshop was equally thought-challenging and diverse.  (I managed to miss the ten first minutes by taking a Line 3 train to 125th street, having overlooked the earlier split from Line 1… Crossing south Harlem on a Sunday morning is a fairly mild

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Handbook of Markov chain Monte Carlo

September 21, 2011
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Handbook of Markov chain Monte Carlo

At JSM, John Kimmel gave me a copy of the Handbook of Markov chain Monte Carlo, as I had not (yet?!) received it. This handbook is edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng, all first-class jedis of the MCMC galaxy. I had not had a chance to get a look at

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Numerical analysis for statisticians

August 25, 2011
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Numerical analysis for statisticians

“In the end, it really is just a matter of choosing the relevant parts of mathematics and ignoring the rest. Of course, the hard part is deciding what is irrelevant.” Somehow, I had missed the first edition of this book and thus I started reading it this afternoon with a newcomer’s eyes (obviously, I will

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