Boost Users :
Subject: Re: [Boost-users] [distributions]: Inverse Gamma
From: Thomas Mang (Thomas.Mang_at_[hidden])
Date: 2010-08-03 10:06:52
On 30.07.2010 18:51, Paul A. Bristow wrote:
>> -----Original Message-----
>> From: boost-users-bounces_at_[hidden] [mailto:boost-users-bounces_at_[hidden]] On Behalf Of Thomas Mang
>> Sent: Friday, July 30, 2010 4:29 PM
>> To: boost-users_at_[hidden]
>> Subject: [Boost-users] [distributions]: Inverse Gamma
>> any plans of implementing the inverse gamma distribution as part of the distributions library ?
> This looks possible - but I'm curious about applications - you obviously have one, but Wikipedia doesn't mention any
> But you obviously have one ;-)
Yes I truly have one ;) The inverse gamma distribution and its special
case, the scaled inverse chi-square distribution, is the conjugate prior
to the normal distribution variance parameter in Bayesian statistics.
Pretty much as uncommon and unheard of as it is outside Bayes world [to
the best of my knowledge], it's very much central to Bayesian stats and
appears in every textbook right after the introduction chapter ;)
Hence I wonder it has not been requested so far - but being a Bayesian
C++ / booster I definitely want / need it :).
@John: Yes it is a transformation deviate of the gamma, and an easy so.
And it should be fairly easy to implement IMHO.
Is contribution on my side expected (can be done just notice I am a
[heavy !] user of the stats library only, not familiar with code /
numerical stability issues).
>> What about multivariate (in particular the multivariate normal and t
>> distributions and Wishart and Dirichlet ) ?
> A previous offer to do some multivariate distributions seems have fizzled out - perhaps because it is *much* more
> difficult, particularly with the templated and 'policied ' (to control the troublesome parameter cases) structure used
> for the Boost.Math library. There may be no analytic expressions for some like inverse and CDF.
> So some strong motivation and support would be needed to embarge on this.
I think the multivariate normal is fairly obvious, use for the others
equally arises in Bayesian statistics. But I understand that everything
is much more complicated.
What about 'vegetarian' versions offering say only the pdf (that is
needed a lot. Personally I have never had the need for CDFs / inverse
CDFs as these are truly a big mess. But the pdf - yes would be cool).
> Paul A. Bristow
> Prizet Farmhouse
> Kendal, UK LA8 8AB
> +44 1539 561830, mobile +44 7714330204
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