[R] Bayesian data analysis recommendations

[Matt Shotwell]

On Thu, 2012-01-19 at 19:23 -0500, C W wrote:
> Thanks, Rich, I will look at the book.
> 
> I agree, there are many nice packages, but what if the package changes in a
> few years?  I would have no idea what is going on!  I've heard
> from predecessor in the industry who emphasize the learning, not just plug
> and chug.
> 
> I really want to learn the material and understand it, above all, it is
> interesting.
> 
> I am looking more towards Bayesian statistics or Bayesian inference.  I am
> in statistics graduate school, though not my field, the biology application
> could help in the understand I suppose?

This list (r-help) may not be the best place to look for advice on this.
But here is some anyway :)

For a well-rounded introduction, I recommend Robert's 'The Bayesian
Choice'. This is a great foundation for Bayesians who intend to defend
their positions on statistical inference. For a more practical approach,
Gelman, Carlin, Stern, and Rubin's book 'Bayesian Data Analysis' has
been very popular (THE most popular, according to some). Regarding the
software tools for Bayesian data analysis, the most mature _and_ active
_and_ best integrated with the R project is Martyn Plummer's JAGS (See
also the R package rjags, by the same author). Another tool that I'm
planning to check out is PyMC: http://code.google.com/p/pymc/

Best,
Matt

> On Thu, Jan 19, 2012 at 7:07 PM, Rich Shepard <rshepard at appl-ecosys.com>
> wrote:
> > On Thu, 19 Jan 2012, C W wrote:
> >
> >> I am trying to learn Bayesian inference and Bayesian data analysis, I am
> >> new in the field.  Would any experts on the list recommend any good sites
> >> or materials for beginners?
> >>
> >> My approach is to learn and understand the theory first, then program
> >> on my own using R, though I see there are already packages.
> >
> >
> >  I'm far from an expert, but why not avoid re-inventing the wheel while
> you
> > learn? Buy and read Jim Albert's "Bayesian Computation with R".
> >
> >  If you're a population ecologist (or willing to extend pesented examples
> > and ideas to communities and ecosystems), Ben Bolker's "Ecological Models
> > and Data in R" explains when Bayesian and frequentist approaches each have
> > advantages over the other.
> >
> > Rich
> >
> > ______________________________________________
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