[SciPy-User] convolution density kernel

Johannes Radinger JRadinger@gmx...
Tue Jan 17 03:35:57 CST 2012


I'd like to apply a 'convolution' on a probability density kernel.
Like in: http://graphics.stanford.edu/courses/cs178/applets/convolution.html

where f=g (for step 1)

In my case the function (base and that for the convolution) is:

def pdf(x,sigma_stat,sigma_mob):
    return (p * stats.norm.pdf(x, loc=m, scale=sigma_stat) + (1-p) * stats.norm.pdf(x, loc=m, scale=sigma_mob))

this are actually two superimposed normal distributions. Due to computational
problems I think it easier to sample finely as I just want to represent the result using matplotlib. Furthermore I'd like to do this convolution computation several times, where the output of the last convolution is the input (f) of the next step (function (g) stays the same original one).

Is there any easy applicable function in scipy to to this convolution? As I am not that familiar with scipy hopefully someone can help me in this case.

Best regards,
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