Hello,
it would indeed be very useful to add support for random number generation
for the multivariate Normal and Wishart distributions in the GSL.
I recently obtained C code from colleagues who published an article using
the GSL (Jia and Jannink, Genetics, 2012). It hence is under the GPL, too.
In their code, they added 2 functions, one for multivariate Normal and one
for Wishart.
How can I add these 2 functions to the GSL? Can I simply create files ready
to be included into the GSL and send them to this mailing list?
Moreover, the original authors didn't provide any test. So I guess one
would have to add this. So I had a quick look into "tree/randist/test.c" in
the git repo, but didn't quite understand how "testMoments()" is working. I
can't be considered as a solid C programmer... I can give it a try, but
would anyone be willing to help?
Timothée Flutre
2016-06-21 18:00 GMT+02:00 <address@hidden>:
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Today's Topics:
1. Question about the GSL random number generator-wishart (yorkey)
----------------------------------------------------------------------
Message: 1
Date: Tue, 21 Jun 2016 10:38:33 +0800
From: yorkey <address@hidden>
To: address@hidden
Cc: yorkey <address@hidden>
Subject: [Help-gsl] Question about the GSL random number
generator-wishart
Message-ID: <address@hidden>
Content-Type: text/plain; charset=gb2312
Dear Sir or Madam,
I am using the GSL random number generator to sample cases from
various distributions in my project. It is great to find that GSL supports
lots of distributions. However, neither ?wishart? nor ?inverse wishart? has
been found in GSL. In Gibbs sampling, we always draw samples from the
inverse wishart distribution, which is the conjugate prior for the
covariance matrix of the multivariate gaussian distribution. Notice that
the multivariate gaussian and the related conjugate prior distributions are
frequently employed in Bayesian analysis, I want to know: is there any
alternative implementation of wishart is GSL? Or is there any version under
development supporting the inverse wishart distribution?
Thanks a lot for your kind attention.
Best,
yorkey
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