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From "Patrick Meyer" <meyer...@gmail.com>
Subject RE: [Math] MATH-816 (mixture model distribution) . .
Date Thu, 18 Oct 2012 12:13:52 GMT
I vote for simplicity. Current practice in the social sciences is to fit
multiple models, each with a different number of components, and use fit
statistics to choose the best model. 

There are some additional features I would like to see added and I have the
code to contribute if it is not currently there. To be consistent with
Mplus, we need have the algorithm use multiple random starts and run a few
of the best starts to completion. Mplus uses this strategy to effectively
overcome local minima.


-----Original Message-----
From: Becksfort, Jared [mailto:Jared.Becksfort@STJUDE.ORG] 
Sent: Wednesday, October 17, 2012 11:37 PM
To: Commons Developers List
Subject: RE: [Math] MATH-816 (mixture model distribution) . .

I see.  I am planning to submit the EM fit for multivariate normal mixture
models in the next couple of weeks (Math-817).  A Gibbs sampling DP fit may
be a bit further out.   I am not opposed to allowing the number of
components to change, but I also like the simplicity of this class.
Whatever you guys decide is probably fine.

Jared
________________________________________
From: Ted Dunning [ted.dunning@gmail.com]
Sent: Wednesday, October 17, 2012 9:41 PM
To: Commons Developers List
Subject: Re: [Math] MATH-816 (mixture model distribution)
=?utf-8?B?LiAgICAu? ==?utf-8?B?LiAgICAu?=

The issue is that with a fixed number of components, you need to do multiple
runs to find a best fit number of components.  Gibbs sampling against a
Dirichlet process can get you to the same answer in about the same cost as a
single run of EM with a fixed number of models.

On Wed, Oct 17, 2012 at 7:31 PM, Becksfort, Jared <
Jared.Becksfort@stjude.org> wrote:

> Ted,
>
> I am not sure I understand the problem with the fixed number of 
> components.  My understanding is that CM prefers immutable objects. 
> Adding a component to an object would require reweighting in addition 
> to modifying the component list.  A new mixture model could be 
> instantiated using the getComponents function and then adding or 
> removing more components if necessary.
>
> Jared
> ________________________________________
> From: Ted Dunning [ted.dunning@gmail.com]
> Sent: Wednesday, October 17, 2012 5:21 PM
> To: Commons Developers List
> Subject: Re: [Math] MATH-816 (mixture model
> distribution)=?utf-8?B?LiAgICAu?    =
>
> Seems fine.
>
> I think that the limitation to a fixed number of mixture components is 
> a bit limiting.  So is the limitation to a uniform set of components.  
> Both limitations can be eased without a huge difficultly.
>
> Avoiding the fixed number of components can be done by using some 
> variant of Dirichlet processes.  Simply picking k_max relatively large 
> and then using an approximate DP over that finite set works well.
>
> That said, mixture models are pretty nice to have.
>
> On Wed, Oct 17, 2012 at 2:13 PM, Gilles Sadowski < 
> gilles@harfang.homelinux.org> wrote:
>
> > Hello.
> >
> > Any objection to commit the code as proposed on the report page?
> >   https://issues.apache.org/jira/browse/MATH-816
> >
> >
> > Regards,
> > Gilles
> >
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