Introductory resources on bayesian modeling for cognitive sciences On Cross Validated there is a great question about best introductory books for bayesian statistics. Also, Jeromy Anglim blogged recently about use of JAGS, rjags, and Bayesian Modelling, with some very nice collection of tutorials relevant to the above question. Lots of those resources are single-shot tutorials, covering just some limited scope of programming and modelling. 

In terms of resources that cover a broader range of topics with some background information and coding tutorials, only two sources stand out from the list:


Michael Lee and Eric Wagenmakers Course in Bayesian Graphical Modeling for Cognitive Science, and 


free and aimed at the beginners, but it's a bit rough (looks like a draft, which authors kind of confirm on their website).

John Kruschke Doing Bayesian Data Analysis: A Tutorial with R and BUGS.  


I can't check (cause' someone stole it from my library), but opinions about it are highly positive.



Those two books could potentially hit the spot in terms of sufficient coverage of basic needs for beginner bayesian acolyte. 

What else do you advise as a simple, practical, compact, and thorough introduction to bayesian modeling for a cognitive scientist?
+1 to Speldosa's suggestion. Griffiths and colleagues have written several primers on the use of Bayesian  models in cogsci. Many of them can be found on Griffiths' website under 'Foundations':

http://cocosci.berkeley.edu/publications.php?topic=Foundations

e.g.


  Perfors, A., Tenenbaum, J.B., Griffiths, T. L., &amp; Xu, F. (2011). A
  tutorial introduction to Bayesian models of cognitive development.
  Cognition, 120, 302-321.
  
  Griffiths, T. L., &amp; Yuille, A. (2008). A primer on probabilistic
  inference. In M. Oaksford and N. Chater (Eds.). The probabilistic
  mind: Prospects for rational models of cognition. Oxford: Oxford
  University Press.