On Tue 18 Mar, Kyle Kastner wrote: > This is a little different, but maybe you can take the concepts from the > link below and adapt to use scikit-learn? Thanks for the link Kyle! It was an interesting read. As I understand it, they fit a parametric model to their sets of measurements. However as I don't have a model to fit to my data I need a non-parametric estimator, which is what the KDE does for me.
I should clarify that my ensemble of experiments have the same control parameters (within error). What I want to calculate is the expected pdf for a new experiment, given the observations that I have. > Thanks for the notebook - some really cool visualizations in there! I look > forward to hearing more about this. It's much easier to think when you have pretty pictures :) aaron ------------------------------------------------------------------------------ Learn Graph Databases - Download FREE O'Reilly Book "Graph Databases" is the definitive new guide to graph databases and their applications. Written by three acclaimed leaders in the field, this first edition is now available. Download your free book today! http://p.sf.net/sfu/13534_NeoTech _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
