# Posts Tagged ‘ summary statistics ’

## ABC [PhD] course

January 25, 2012
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As mentioned in the latest post on ABC, I am giving a short doctoral course on ABC methods and convergence at CREST next week. I have now made a preliminary collection of my slides (plus a few from Jean-Michel Marin’s), available on slideshare (as ABC in Roma, because I am also giving the course in

## semi-automatic ABC

December 17, 2011
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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

## Selecting statistics for ABC model choice [R code]

November 1, 2011
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As supplementary material to the ABC paper we just arXived, here is the R code I used to produce the Bayes factor comparisons between summary statistics in the normal versus Laplace example. (Warning: running the R code takes a while!) Filed under: R, Statistics, University life Tagged: ABC, Bayesian model choice, Laplace distribution, R, summary

## Proc report for simple statistics

October 30, 2011
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Ken Beath, of Macquarie University, commented on an earlier entry that the best way to generate summary statistics is using proc report. While the best tools might differ, depending on the purpose, we wanted to share Ken's code demonstrating how to re...

## Approximate Bayesian computational methods on-line

October 25, 2011
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Fig. 4 – Boxplots of the evolution of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time

## expectation-propagation and ABC

August 23, 2011
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$expectation-propagation and ABC$

“It seems quite absurd to reject an EP-based approach, if the only alternative is an ABC approach based on summary statistics, which introduces a bias which seems both larger (according to our numerical examples) and more arbitrary, in the sense that in real-world applications one has little intuition and even less mathematical guidance on to

## About Fig. 4 of Fagundes et al. (2007)

July 12, 2011
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Yesterday, we had a meeting of our EMILE network on statistics for population genetics (in Montpellier) and we were discussing our respective recent advances in ABC model choice. One of our colleagues mentioned the constant request (from referees) to include the post-ABC processing devised by Fagundes et al. in their 2007 ABC paper. (This paper

## Lack of confidence [revised]

April 21, 2011
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Following the comments on our earlier submission to PNAS, we have written (and re-arXived) a revised version where we try to spell out (better) the distinction between ABC point (and confidence) estimation and ABC model choice, namely that the problem was at another level for Bayesian model choice (using posterior probabilities). When doing point estimation

## ABC model choice not to be trusted

January 26, 2011
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This may sound like a paradoxical title given my recent production in this area of ABC approximations, especially after the disputes with Alan Templeton, but I have come to the conclusion that ABC approximations to the Bayes factor are not to be trusted. When working one afternoon in Park City with Jean-Michel and Natesh Pillai

## R Tutorial Series: Summary and Descriptive Statistics

November 1, 2009
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Summary (or descriptive) statistics are the first figures used to represent nearly every dataset. They also form the foundation for much more complicated computations and analyses. Thus, in spite of being composed of simple methods, they are essential ...