Bayesian statistics tutorial I am trying to get upto speed in Bayesian Statistics. I have a little bit of stats background (STAT 101) but not too much - I think I can understand prior, posterior, and likelihood :D. 
I don't want to read a Bayesian textbook just yet.
I'd prefer to read from a source (website preferred) that will ramp me up quickly. Something like this, but that has more details.
Any advice?
 A: If you'd like to try a few learn by examples, you may be interested in "Bayesian Computation in R" by Jim Albert.
Its related R package is called LearnBayes. 
A: Some more depth:


*

*http://math.tut.fi/~piche/bayes/notes01.pdf covers Bayes' theorem

*https://ccrma.stanford.edu/~jos/bayes/bayes.pdf and 

*http://www-personal.une.edu.au/~jvanderw/Introduction_to_Bayesian_Statistics1.pdf are more about statistical applications

A: These aren't complete tutorials on Bayesian statistics, but rather isolated explanations of individual concepts that I like. Just thought I'd add in case it helps.


*

*http://lesswrong.com/lw/2b0/bayes_theorem_illustrated_my_way - Graphical explanation of Bayes' theorem.

*What's the difference between a confidence interval and a credible interval? - Great example of the difference between confidence intervals and Bayesian credible intervals (and of the difference between frequentist statistics and Bayesian statistics in general).

*http://behind-the-enemy-lines.blogspot.com/2008/01/are-you-bayesian-or-frequentist-or.html - A simple example of how frequentists and Bayesians approach a problem differently, and how it leads to a different answer.

A: I wrote a post on getting started with JAGS for Bayesian modelling. If you're keen to get started quickly then playing around with some variant of BUGS, such as JAGS, is a practical way to get started.  
To quote the abstract of the post

This post provides links to various resources on getting started with
  Bayesian modelling using JAGS and R. It discusses: (1) what is JAGS;
  (2) why you might want to perform Bayesian modelling using JAGS; (3)
  how to install JAGS; (4) where to find further information on JAGS;
  (5) where to find examples of JAGS scripts in action; (6) where to ask
  questions; and (7) some interesting psychological applications of
  Bayesian modelling.

In particular, you may find it useful to study some of the example scripts mentioned in the post.
A: You could try 'Teaching Bayesian Reasoning In Less Than Two Hours'.
A: The Bayes' rule guide on Arbital is the best resource I've ever found by a good margin.
I like how they emphasize the odds form, include good visualizations, talk about how Bayesianism relates to philosophy, and include different learning paths depending on your background and interests.
A: Here's a place to start:
ftp://selab.janelia.org/pub/publications/Eddy-ATG3/Eddy-ATG3-reprint.pdf
http://blog.oscarbonilla.com/2009/05/visualizing-bayes-theorem/
http://yudkowsky.net/rational/bayes
http://www.math.umass.edu/~lavine/whatisbayes.pdf
http://en.wikipedia.org/wiki/Bayesian_inference
http://en.wikipedia.org/wiki/Bayesian_probability
Tutorial_on_Bayesian_Statistics_and_Clinical_Trials
