Hi,
I have recently started to update the users guide on special
functions, providing accuracy measurements of our implementations.
To get these figures, I carried out extensive comparisons with
reference values computed with Maxima, and 128 decimal digits.
I also wrote a Java command-line app to a
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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 dimensi
On 10/18/12 3:20 PM, Gilles Sadowski wrote:
> Hello.
>
> There is a "getDimensions" method in "AbstractMultivariateRealDistribution"
> that returns the number of variables.
> It would be useful to have this method declared in the interface too.
+1
I might slightly favor "getDimension" as the name,
Hello.
There is a "getDimensions" method in "AbstractMultivariateRealDistribution"
that returns the number of variables.
It would be useful to have this method declared in the interface too. [In
particular, this would allow the mixture model generic class (cf. MATH-816)
to be defined in terms of t
I just realized that when we defined RealMultivariateDistribution we
neglected to actually model the distribution function. If we
forced ourselves to support this and provide an integration-based
default in the abstract base class, that might also force us to
implement some useful multivariate in
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
Existing code does have a certain cachet to it.
On Thu, Oct 18, 2012 at 5:13 AM, Patrick Meyer 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.
>
> T
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
> >
On Thu, Oct 18, 2012 at 11:00:17AM -0400, Patrick Meyer wrote:
> Yes, I like the latest changes. It looks cleaner to me.
>
> It seems that the attachments only describe the mixture distribution and do
> not provide the EM estimation algorithm. Am I missing something? Didn't the
> original contrib
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Yes, I like the latest changes. It looks cleaner to me.
It seems that the attachments only describe the mixture distribution and do
not provide the EM estimation algorithm. Am I missing something? Didn't the
original contributor mention the estimation part too? The parts I would like
to add will
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 samp
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On Thu, Oct 18, 2012 at 08:13:52AM -0400, Patrick Meyer 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.
So... Do you vote for the current proposal (as
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 curren
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Hi.
Please have a look at my last comment in
https://issues.apache.org/jira/browse/MATH-868
Thanks,
Gilles
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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
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