Hi all,
I can see BUGS made modeling easy, but there is still a learning curve
for me. That's because I am mostly a software guy who wants to learn
BUGS recently, because of work needs.
Being not a statistician, I haven't taken classes in Bayesian, though
I have taken classes in basic statistics and I roughly know how to use
R and Matlab for statistics use.
I have searched the Internet extensively, however the tutorial I came
across are of the following types:
(1) Assuming statistics background and focusing on BUGS: I couldn't
follow these types of tutorials and examples, such as the "Schools"
and "Birats" examples, because I am not familiar with their
statistical background and they are very terse on the stats part ...
(2) Assuming domain background and focusing on application: For
example, they are for biostats and medical people, so the examples
there are too complicated and with lots of jargons, which makes it too
time consuming for me to digest the domain specific context instead of
focusing on stats and BUGS...
(3) Great books for Bayesian computation in general and use R for examples...
So are there some study materials that give non-domain specific
statistic examples, and elaborate on both statistics background and
BUGS usages? For example, if the example uses the term "probit
regression", then at least includes some elaboration on what is the
"probit regression" and how is it traditionally done without using
BUGS and/or without using Bayesian and how is it done now using
Bayesian and using BUGS...?
Please give me some pointers!
Thanks!
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