The purpose of the task view is to answer questions like this. I for one would not be able to give a better answer than what is there.
My suggestion would be to pull out your Bayesian textbook (or get one, or use online notes from a class, etc.) and look through the homework problems and examples for things that may be similar to what you may be doing in the future. Then try doing those problems/examples using a few of the different tools recommended in the task view (start with simple things even if they are not the types of problems you will do for real, just to get a feel for the different packages). This will help you decide which packages work best for you and which interfaces you prefer. Then when you have real problems to solve, you will have the knowledge of which tools to use and how to use them. You should also consider contributing what you learn back to the task view for others. -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.s...@imail.org 801.408.8111 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r- > project.org] On Behalf Of Ben > Sent: Tuesday, April 13, 2010 5:50 PM > To: r-help@r-project.org > Subject: [R] Getting Started with Bayesian MCMC > > Hi all, > > I would like to start to use R's MCMC abilities to compute answers in > Bayesian statistics. I don't have any specific problems in mind yet, > but I would like to be able to compute/sample posterior probabilities > for low-dimensional custom models, as well as handle "standard" > Bayesian cases like linear regression and hierarchical models. > > R clearly has a lot of abilities in this area: > > http://cran.r-project.org/web/views/Bayesian.html > > --enough to be confusing! For instance, there are apparently three > separate interfaces to JAGS, and I'm not even sure I want/need to > interface to JAGS at all. > > Can someone please get me started? Are there a handful of "must-have" > packages and software that everyone (who uses MCMC regularly) uses? > > Any responses are appreciated, > > -- > Ben > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
