Fully Bayesian Computing

http://www.stat.columbia.edu/~gelman/research/unpublished/fullybayesiancomputing-nonblinded.pdf

by mgasner • 15 years ago

53 7 15 years ago

stat.columbia.edu

wnoise• 15 years ago

See also "Probabilistic Functional Programming" at http://www.haskell.org/haskellwiki/Probabilistic_Functional_...

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.

ekidd• 15 years ago

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...

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

alphadogg• 15 years ago

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

robertk• 15 years ago

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

giardini2• 15 years ago

No. Be pleased that it is not "posterior stimulations".

Butt otherwise, remember that this is not Reddit. Kindly keep comments like this to a minimum.

MrMan• 15 years ago

It is wrong on HN. On digg.com it might be ok. Why is your comment grey?

giardini2• 15 years ago

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

Fully Bayesian Computing

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