This is a little different, but maybe you can take the concepts from the
link below and adapt to use scikit-learn? Seems like you have a similar
situation: many experiments, all parameters should be similar, but not
strictly identical. I am not sure how to combine that with the sklearn KDE,
but I am interested to hear more.
Hierarchical Linear Regression using PyMC3:
http://twiecki.github.io/blog/2014/03/17/bayesian-glms-3/
Thanks for the notebook - some really cool visualizations in there! I look
forward to hearing more about this.
Kyle
On Tue, Mar 18, 2014 at 7:37 AM, Aaron O'Leary <[email protected]>wrote:
> I'm slightly out of my depth on the theory here. I've been playing with
> Kernel Density Estimation on my data and I'm getting good results. What
> I would like to do is extend this to ensemble measurements.
>
> I have data collected from n realisations of a lab experiment*. The data
> is a time series of velocities, with N data points per ensemble member.
> n~15, N~2500000
>
> I have made a kde for a single set of data, using KernelDensity+. My
> naive approach for creating an ensemble estimator is to bundle all the
> ensemble data together and fit the kde to that. However I am aware that
> this is going to be overfitted.
>
> I would like to make an ensemble kde for this data. I'm thinking that I
> need to cross validate the esimators somehow. My first thought is doing
> a leave one out cross validation on the ensemble data, but I'm sure
> there is a more appropriate way to do it.
>
> I'm looking at the methods in sklearn.ensemble, but I'm a bit lost as to
> what I should use. I feel I am a bit constrained computationally by the
> quantity of data that I have.
>
> Any input here would be appreciated.
>
> cheers,
> aaron
>
>
>
> * for interest, it is a fluids experiment looking at turbulence with
> piv. You can get a flavor here:
>
> http://nbviewer.ipython.org/github/aaren/notebooks/blob/master/2d_pdf.ipynb
>
> + i.e. sklearn.neighbours.KernelDensity. thanks @jakevdp for the write
> up!
>
>
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