jackman.stanford.edu/blog
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conference in honor of Paul Sniderman

Saturday October 29, 2011

Filed under: general — jackman @ 4:19 pm

Steve Haber organized a wonderful event at Stanford yesterday, honoring Paul Sniderman.

It was especially “splendid” (as Paul might say) to be able to honor Paul in this way, in the presence of his family.

Steve was assisted by Mike Tomz, Jonathan Rodden and myself on the planning of the event. Our front office staff help execute the complicated logistics superbly (thanks in particular to Jackie Sargent, Eliana Vasquez — and happy birthday, today, Eliana — and Judy Low).

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pscl 1.04 live on CRAN

Saturday October 15, 2011

Filed under: computing,R,statistics — jackman @ 5:01 pm

Update to my pscl package, now on CRAN.

Biggest change: fixing a bug in the way MCMC draws for item parameters were being stored and summarized by ideal.

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happiness #1

Thursday October 13, 2011

Filed under: general — jackman @ 9:00 am

Swimming with Janet and the kids, lovely deep clear water in the nearly-empty diving pool, everyone’s chasing everyone, googles on,
giggles and smiles clearly visible.

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Bay Area R Users group has 1300 members

Wednesday October 12, 2011

Filed under: R,statistics — jackman @ 3:03 pm

Impressive.

You are not alone!

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Brad Efron meets Clarify?

Monday October 3, 2011

Filed under: computing,statistics — jackman @ 11:36 am

This might be an interesting seminar. I wonder if Brad knows about the long history of “poor-person’s” (approximately-asymptotically valid) Bayesian inference in political science via things like Clarify?

Tuesday, October 4, 4:15pm: Statistics Seminar, Sequoia Hall Room 200
Brad Efron, Stanford University Statistics
“Bayesian inference and the parametric bootstrap”

Abstract:
The parametric bootstrap can often be used to compute posterior distributions obtained from complicated Bayesian models. Besides its computational advantages, the bootstrap o ffers insight on the relationship between Bayesian and frequentist methods. I will discuss some examples relating to exponential families, generalized linear models, and high-dimensional inference.

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