Typing this on my phone. Sorry about format. The sampling part of the mixture model makes it a true distribution according to the abstract class and interface. It will also come in handy for simulating data, I think. I am already using it to simulate mri images. _
I support adding get dimension function to interface. I can't do anything for a few days though. _______________________________________ From: Phil Steitz [phil.ste...@gmail.com] Sent: Thursday, October 18, 2012 2:50 PM To: Commons Developers List Subject: Re: [Math] MATH-816 (mixture model distribution)=?utf-8?B?LiAgICAu? = On 10/18/12 8:55 AM, Gilles Sadowski wrote: > On Thu, Oct 18, 2012 at 06:59:22AM -0700, Phil Steitz wrote: >> On 10/18/12 1:41 AM, Gilles Sadowski wrote: >>> On Wed, Oct 17, 2012 at 10:26:55PM -0700, Phil Steitz wrote: >>>> On 10/17/12 8:36 PM, Becksfort, Jared wrote: >>>>> 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. >>>> I like the interface as implemented for what it represents, >>> By "interface", do you mean the class >>> MixtureMultivariateRealDistribution" >>> as implemented in the file on the JIRA page? >> Yes, the most recent one. I like the way you set up the >> constructors, handling the weights and distribution type parameter. >>>> but I >>>> agree with Ted's point above. I also wonder if implementing the >>>> multivariate distribution interface is really buying you anything. >>>> Certainly not for the Gibbs sampler. It might be better to just >>>> directly implement EM with an interface that is natural for fitting >>>> and using mixture models. I am not sure this stuff belongs in the >>>> distribution package in any case. >>> As implemented, it seems quite natural. >>> How this class will be used by non-existing code is beyond the scope of >>> MATH-816. >>> [And when the code exists, we can always revisit the design if necessary.] >> It works for fixed component models, which I guess is OK by >> consensus to start. The question I was asking is what exactly do you >> get by having it extend the multivariate real distribution? > Is it not a kind of distribution? > [It's obvious that one can sample from it but maybe there are some required > properties (for a distribution) which are missing from such a mixture (?).] What is implemented is a legitimate distribution (or more precisely, a legitimate density, which is all we really model in RealMultivariateDistribution). I just wonder whether there is value in it as a distribution per se, rather than just a container for the weights and component distribution parameters. The sample() implementation is legitimate - I just don't know if it has any practical value. I guess the density will be used by the EM impl. As I said above, I am fine committing and then seeing how the EM impl uses the class. Assuming it does turn out to be practically valuable as a distribution, a natural thing to add would be a univariate version; but that would require an actual distribution function. Phil > >> I guess >> that will become clear when we get the EM implementation. > Hopefully. > >> I am OK committing this, I just wanted to get a clearer picture of >> how the class was going to be used. > I wouldn't be able to answer. > > > Gilles > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > > --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org Email Disclaimer: www.stjude.org/emaildisclaimer Consultation Disclaimer: www.stjude.org/consultationdisclaimer --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
