bayesclasses-general

Re: [Bayes++] Code issues

[Michael S. <ma...@mi...>]

On Mittwoch 21 September 2005 00:09, you wrote:
> Great work! :)
>
> I have also been unable to find a method for generating samples from the
> models. It would have been great to be able to get samples with desired
> covariance, based on prediction- and observation models. Typical usage
> would be to simulate a process, and to generate observations from it.

There is a Gaussian sampler as a helper class for in the SIR filter. In 
"SIRFlt.hpp" I define the class template 'Sampled_general_predict_model'
and the two predefine classes 'Sampled_LiAd_predict_model' 
'Sampled_LiInAd_predict_model'.

These convert Linear or Linrz predict models into 'Sampled_predict' models. 
You define the Gaussian covaiance by the values of G and q in the predict 
model. This is much the same as your 'SampleCorelated' function. Except I 
think I have the spelling correct in this case :-)

In the case of observation models Bayes++ has model generalisers in 
"models.hpp". For example 'General_LiUnAd_observe_model' generalises a Linear 
Uncorrelated Addative noise observe model. The generalised model is also a 
'Likelihood_observe_model' and so the likelihood of a state given an 
obervation can be computed with the 'L' function. This allows you to use a 
Gaussian noise models to resample, for example in the 'SIR_scheme'.

Best regard, and thanks for the feedback,
	Michael
-- 
___________________________________
Michael Stevens Systems Engineering

34128 Kassel, Germany
Phone/Fax: +49 561 5218038

Navigation Systems, Estimation  and
                 Bayesian Filtering
    http://bayesclasses.sf.net
___________________________________

Re: [Bayes++] Code issues

[Fredrik O. <fre...@id...>]

Sorrry that I didn't notice your "SIR" classes. They seem to do exactly what 
I've wanted :). Once again, thanks for your great work! :)

The abstract "SIR_random" class does however still need an "default", and 
easily accessible implementation. The "Boost_SIR_random_helper" class found 
in Matlab/matlabBfilter.hpp and MuPad/bfilter.cpp seems to be a great 
candidate for a default random-implementation. Would it be possible to 
extend this class with the (optional) possibility of proper seeding, and 
include it in "SIRFlt.hpp" (or other file in "BayesFilter/")? This would 
help avoid the need for creating a custom RNG for each project (, like 
Rtheta_random, Test_random, SLAM_random, etc.), as they all contain 
basically the same implementation.

As pointed out in a previous mail; I would gladly assist you in any future 
development of Bayes++, if you're interrested in any help.

regards,
Fredrik Orderud
Ph.D student in Computer Science, NTNU

----- Original Message ----- 
From: "Michael Stevens" <ma...@mi...>
To: <bay...@li...>
Sent: Saturday, September 24, 2005 10:49 AM
Subject: Re: [Bayes++] Code issues


>> I have also been unable to find a method for generating samples from the
>> models. It would have been great to be able to get samples with desired
>> covariance, based on prediction- and observation models. Typical usage
>> would be to simulate a process, and to generate observations from it.
>
> There is a Gaussian sampler as a helper class for in the SIR filter. In
> "SIRFlt.hpp" I define the class template 'Sampled_general_predict_model'
> and the two predefine classes 'Sampled_LiAd_predict_model'
> 'Sampled_LiInAd_predict_model'.
>
> These convert Linear or Linrz predict models into 'Sampled_predict' 
> models.
> You define the Gaussian covaiance by the values of G and q in the predict
> model. This is much the same as your 'SampleCorelated' function. Except I
> think I have the spelling correct in this case :-)
>
> In the case of observation models Bayes++ has model generalisers in
> "models.hpp". For example 'General_LiUnAd_observe_model' generalises a 
> Linear
> Uncorrelated Addative noise observe model. The generalised model is also a
> 'Likelihood_observe_model' and so the likelihood of a state given an
> obervation can be computed with the 'L' function. This allows you to use a
> Gaussian noise models to resample, for example in the 'SIR_scheme'.
>
> Best regard, and thanks for the feedback,
> Michael