Tue Jul 7 20:36:29 CDT 2009
On Tue, Jul 7, 2009 at 6:28 AM, Joshua Stults<email@example.com> wrote:
> I was wondering if scipy had something similar to Octave/Matlab's
> empricial_rnd(). ?Here's the blurb from Octave's help describing the
> ?-- Function File: ?empirical_rnd (N, DATA)
> ?-- Function File: ?empirical_rnd (DATA, R, C)
> ?-- Function File: ?empirical_rnd (DATA, SZ)
> ? ? Generate a bootstrap sample of size N from the empirical
> ? ? distribution obtained from the univariate sample DATA.
> ? ? If R and C are given create a matrix with R rows and C columns. Or
> ? ? if SZ is a vector, create a matrix of size SZ.
> So basically you pass it an array of data, and it returns bootstrap
> samples (resampling from the array with replacement).
Be very careful and be certain you can derive the statistical
justification for what you are doing when you use bootstrap. There
are numerous cases in which bootstrapping will not give you the right
answer, such as when fitting a function that has a parameter that is
set in just a small subset of the data, because in some samples the
subset may be omitted completely or in large part, admitting wildly
wrong parameter values. While you didn't specify exactly what you are
trying to do, for many problems Markov-Chain Monte Carlo is both
better and faster, and is often easier to code. Plus, there is Python
for it (pymc, I think).
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