[SciPy-User] Mean, variance, and parametrisation of an inverse Gaussian distribution

[nicky van foreest]

Hi Mathieu,

I just checked the wikipedia on this distribution. From this and the
info on the sicpy.stats on invgauss I think you should try to use the
loc, scale and shape parameters of invgauss to match your need. The
meaning of loc and scale can be found here:

http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html#shifting-and-scaling

The paragraph below this explains how to use shape parameters. You can
tune these parameters such that the mean is a/\sigma and the variance
is also what you need.

Hope this helps

Nicky

On 28 June 2012 15:33, servant mathieu <servant.mathieu at gmail.com> wrote:
> Dear scipy users,
>
> The time for a diffusion process to reach a single evidence threshold a is
> often modeled as an inverse Gaussian distribution with mean (a/σ) and
> variance (a*σ2/μ3 ), where  μ represents the mean drift rate and  σ2  the
> variance of the accumlulation process. How could I reparametrise the
> scipy.stats.invgauss  function to manipulate those parameters?
>
> Cheers,
> Mathieu
>
>
>
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