
Boost Users : 
Subject: Re: [Boostusers] [distributions]: Inverse Gamma
From: Thomas Mang (Thomas.Mang_at_[hidden])
Date: 20100803 10:06:52
On 30.07.2010 18:51, Paul A. Bristow wrote:
>
>
>> Original Message
>> From: boostusersbounces_at_[hidden] [mailto:boostusersbounces_at_[hidden]] On Behalf Of Thomas Mang
>> Sent: Friday, July 30, 2010 4:29 PM
>> To: boostusers_at_[hidden]
>> Subject: [Boostusers] [distributions]: Inverse Gamma
>>
>> Hi,
>>
>> 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
>
> http://en.wikipedia.org/wiki/Inversegamma_distribution
>
> But you obviously have one ;)
Yes I truly have one ;) The inverse gamma distribution and its special
case, the scaled inverse chisquare 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 ;)
http://en.wikipedia.org/wiki/Scaled_inverse_chisquare_distribution
http://en.wikipedia.org/wiki/Conjugate_prior
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.
>
> Paul
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).
best,
Thomas
>
>
> 
> Paul A. Bristow
> Prizet Farmhouse
> Kendal, UK LA8 8AB
> +44 1539 561830, mobile +44 7714330204
> pbristow_at_[hidden]
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