Posts Tagged ‘ MCMC ’

slides for my simulation course

October 18, 2012
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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

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Introduction to Bayesian Methods guest lecture

October 18, 2012
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Introduction to Bayesian Methods guest lecture

This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides

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structure and uncertainty, Bristol, Sept. 26

September 26, 2012
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structure and uncertainty, Bristol, Sept. 26

Another day full of interesting and challenging—in the sense they generated new questions for me—talks at the SuSTain workshop. After another (dry and fast) run around the Downs; Leo Held started the talks with one of my favourite topics, namely the theory of g-priors in generalized linear models. He did bring a new perspective on

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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #1)

September 21, 2012
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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #1)

As advertised on the ‘Og, the ISBA mailing list and now the birth certificate of BayesComp (!), MCMSki IV is taking place for sure in Chamonix-Mont-Blanc, January 6-8 2014. The webpage has been started, thanks to Merrill Liechty, and should grow with informations about the location, the hotels, registration, transportation, and of course skiing (check

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Montreal R Workshop: Introduction to Bayesian Methods

March 22, 2012
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Montreal R Workshop: Introduction to Bayesian Methods

Monday, March 26, 2012  14h-16h, Stewart Biology N4/17 Corey Chivers, Department of Biology McGill University This is a meetup of the Montreal R User Group. Be sure to join the group and RSVP. More information about the workshop here. Topics Why would we want to be Bayesian in the first place?  In this workshop we

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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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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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principles of uncertainty

October 13, 2011
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principles of uncertainty

“Bayes Theorem is a simple consequence of the axioms of probability, and is therefore accepted by all as valid. However, some who challenge the use of personal probability reject certain applications of Bayes Theorem.“  J. Kadane, p.44 Principles of uncertainty by Joseph (“Jay”) Kadane (Carnegie Mellon University, Pittsburgh) is a profound and mesmerising book on

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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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Example 9.8: New stuff in SAS 9.3– Bayesian random effects models in Proc MCMC

October 4, 2011
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Example 9.8: New stuff in SAS 9.3– Bayesian random effects models in Proc MCMC

Rounding off our reports on major new developments in SAS 9.3, today we'll talk about proc mcmc and the random statement.Stand-alone packages for fitting very general Bayesian models using Markov chain Monte Carlo (MCMC) methods have been available for...

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