pymc-user

[Pymc-user] use TkAgg?

[Hanno K. <kl...@ph...>]

Hi,

I have just started toying around with PyMC. However, I ran directly
in the beginning into a catch: I don't have Tkinter installed for
various reasons.

This throws up an error when importing PyMC, because it wants the
TkAgg backend of matplotlib to be used. However, after changing 

use('TkAgg') 

into 

use('GTK')

in Matplot.py 

and setting interactive=True in matplotlibrc, everything works well.
Is there a deeper reason for "hard-wiring" TkAgg usage into PyMC and
are there further problems down the road I have to anticipate with my
approach?

Thanks,
Hanno


-- 
Hanno Klemm
kl...@ph...

Re: [Pymc-user] use TkAgg?

[Christopher F. <ch...@tr...>]

On Apr 21, 2006, at 5:24 AM, Hanno Klemm wrote:

> Is there a deeper reason for "hard-wiring" TkAgg usage into PyMC and
> are there further problems down the road I have to anticipate with my
> approach?

Hello Hanno,

The only reason TkAgg is hard-wired into PyMC is that it was a better  
default than WxAgg, since Tkinter is easy to install on most systems  
(unlike WXpython), and even comes built in to some python  
distributions, like ActiveState. There should be no problem in  
choosing another backend.

Good luck,
Chris

--
Christopher J. Fonnesbeck

Population Ecologist, Marine Mammal Section
Fish & Wildlife Research Institute (FWC)
St. Petersburg, FL

Adjunct Assistant Professor
Warnell School of Forest Resources
University of Georgia
Athens, GA

T: 727.235.5570
E: chris at trichech.us

Re: [Pymc-user] use TkAgg?

[Hanno K. <kl...@ph...>]

Hi Christopher,

thanks for the quick reply. 

Another question is bothering me at the moment (this time maybe a
little less technical):

I understand that the parameters to be estimated are defined via the
sample.parameter method. There I can define a proposed distribution
via dist='some_dist'. However, 'some_dist' needs to be zero-centered,
if I understand the comment in the code correctly. What is the best
way of dealing with a case where you want to have your parameters
greater-equal than zero? Or am I missing the point completely?

Best regards,
Hanno



Christopher Fonnesbeck <ch...@tr...> said:

> On Apr 21, 2006, at 5:24 AM, Hanno Klemm wrote:
> 
> > Is there a deeper reason for "hard-wiring" TkAgg usage into PyMC and
> > are there further problems down the road I have to anticipate with my
> > approach?
> 
> Hello Hanno,
> 
> The only reason TkAgg is hard-wired into PyMC is that it was a better  
> default than WxAgg, since Tkinter is easy to install on most systems  
> (unlike WXpython), and even comes built in to some python  
> distributions, like ActiveState. There should be no problem in  
> choosing another backend.
> 
> Good luck,
> Chris
> 
> --
> Christopher J. Fonnesbeck
> 
> Population Ecologist, Marine Mammal Section
> Fish & Wildlife Research Institute (FWC)
> St. Petersburg, FL
> 
> Adjunct Assistant Professor
> Warnell School of Forest Resources
> University of Georgia
> Athens, GA
> 
> T: 727.235.5570
> E: chris at trichech.us
> 
> 
> 



-- 
Hanno Klemm
kl...@ph...

Re: [Pymc-user] use TkAgg?

[Christopher F. <ch...@tr...>]

On Apr 21, 2006, at 9:02 AM, Hanno Klemm wrote:
>
> I understand that the parameters to be estimated are defined via the
> sample.parameter method. There I can define a proposed distribution
> via dist='some_dist'. However, 'some_dist' needs to be zero-centered,
> if I understand the comment in the code correctly. What is the best
> way of dealing with a case where you want to have your parameters
> greater-equal than zero? Or am I missing the point completely?

Well, in theory the proposal distribution is arbitrary -- it can  
literally be any distribution over the support of the parameter. In  
practice, however, some distributions work better than others. A  
noncentral proposal distribution is fine, but it may not converge as  
efficiently as a central one. I can't think of a reason a priori that  
you would want to have a nonzero expected value, but perhaps you have  
one.

At the moment, 3 proposal distributions are built-in to PyMC: normal  
(the default), uniform, and double exponential. The double  
exponential proposal is interesting because it will occasionally  
propose extreme values, due to its long tails, which can help avoid  
metastable behaviour.

I plan on releasing, with the official 1.0 version, a more  
comprehensive user's guide that will rather explicitly lay all this out.

Regards,
Chris

--
Christopher J. Fonnesbeck

Population Ecologist, Marine Mammal Section
Fish & Wildlife Research Institute (FWC)
St. Petersburg, FL

Adjunct Assistant Professor
Warnell School of Forest Resources
University of Georgia
Athens, GA

T: 727.235.5570
E: chris at trichech.us