[SciPy-user] Kernel density approximation
Mon Nov 5 12:45:13 CST 2007
What is the recommended way to compute a kernel density approximation of
a probability distribution function given a sample data ? My problem is
two dimensional, each data element is made of a pair of numbers.
I currently use an histogram2d, but I would like to use a smooth kernel,
such as a gaussian, and most important, I would like the algorithm to do
the work for me as far as choosing the bandwidth :->.
And I forgot to say that the function should be fast, as it is used in a
Monte Carlo algorithm, and is called many times.
Yes, I know, I want the Moon, on a stick, please :->.
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