Re: [Math] MATH-816 (mixture model distribution) . .

[Ted Dunning <te...@gmail.com>]

Existing code does have a certain cachet to it.

On Thu, Oct 18, 2012 at 5:13 AM, Patrick Meyer <me...@gmail.com> wrote:

> 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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