

Fully Bayesian Computing - mgasner
http://www.stat.columbia.edu/~gelman/research/unpublished/fullybayesiancomputing-nonblinded.pdf

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wnoise
See also "Probabilistic Functional Programming" at
[http://www.haskell.org/haskellwiki/Probabilistic_Functional_...](http://www.haskell.org/haskellwiki/Probabilistic_Functional_Programming)

A Haskell embedding seems easier than adding new syntax to R, though you don't
get all the nice algorithms that are already implemented in R.

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ekidd
The "Probablistic Functional Programming" paper was really fun, and it builds
on some decades-old observations in mathematics.

You can easily extend the PFP framework to handle Bayes rule. I experimented
with this a few years back, and was pleasantly surprised how natural it was:

[http://www.randomhacks.net/articles/2007/02/22/bayes-rule-
an...](http://www.randomhacks.net/articles/2007/02/22/bayes-rule-and-drug-
tests)

(Or, for those who love details, a PDF:
[http://www.randomhacks.net/darcs/probability-
monads/probabil...](http://www.randomhacks.net/darcs/probability-
monads/probability-monads.pdf) )

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giardini
I would welcome a single-paragraph summarization of the findings.

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alphadogg
Is it wrong to read a journal paper and keep giggling at "posterior
simulations"?

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robertk
This is not Reddit. Kindly keep comments like this to a minimum.

