[SciPy-User] Joint distributions

josef.pktd@gmai... josef.pktd@gmai...
Wed Mar 21 13:07:51 CDT 2012

On Wed, Mar 21, 2012 at 1:48 PM, William Furnass <will@thearete.co.uk> wrote:
> I am wanting to fit a parameterised model to a series of 15
> datapoints, with each being a concentration C and time t.  Within the
> objective function of the optimisation routine that I'm using for the
> model fitting I presently calculate fitness using the Bray Curtis
> distance between the data series and the prediction corresponding to a
> candidate solution.
> I would ideally like to calculate fitness in such a way as to account
> for uncertainty in each (C, t).  I think I can achieve this for a
> given data series by
>  a) modelling each data point using a bivariate Gaussian PDF (with
> static variances for both C and t)
>  b) calculate a prediction using a small dt
>  c) find the highest probability of all points in the prediction
> series for each of the 15 bivariate PDFs
>  d) sum or average the probabilities to get a measure of the fit of
> the real data series to the prediction corresponding to the candidate
> solution.
> My question is is there an easy way of finding joint probabilities
> using scipy.stats?  I thought I could construct a bivariate normal
> distribution using
> dens = scipy.stats.norm(loc=np.array([t[i], C[i]]),
> scale=np.array([t_stdev, C_stdev]))
> but
> dens.pdf(np.array([5,7]))
> returns an array when I thought it should return a scalar probability.

scipy.stats only has univariate distributions, or to be exact it
calculates it for many points independently.

So the returned array is the pdf for each point separately calculated.

If you want the pdf for the bivariate or multivariate normal
distribution then it's just a few lines,
( I think the bivariate normal is also in matplotlib, in statsmodels ?)

Your fitting problem sounds a bit like what scipy.odr does.


> Apologies if the above is not particularly clear or if I'm missing
> something obvious here.
> Regards,
> Will Furnass
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