[SciPy-User] generic expectation operator

[nicky van foreest]

Hi,

For numerous purposes I need to compute the expectation of some
function with respect to some probability distribution.  i.e. E f(X) =
\int f(x) dG(x), where f is the function and G the distribution. When
G is continuous, this is simple, just use scipy.integrate.quad.
However, if G is discrete, things become more complicated since quad
often misses the spikes in the density. To deal with this problem, in
part, I came up with the following code, but it is not completely ok
yet, see below.

#!/usr/bin/env python

import scipy.stats as stats
import scipy.integrate
from math import sqrt

def E(f, X):
    if hasattr(X,'dist'):
        if isinstance(X.dist, stats.rv_discrete):  # hint from Ralph
            g = X.pmf
        else:
            g = X.pdf
        return scipy.integrate.quad(lambda x: x*g(x), X.dist.a, X.dist.b)
    else:
        return sum(f(x)*p for x,p in zip(X.xk, X.pk))

# now the following works:
X = stats.norm(3,sqrt(3))
print E(sqrt, X)

# this is ok too
grid = [50, 100, 150, 200, 250, 300]
probs=[2,3,2,1.,0.5,0.5]
tot = sum(probs)
probs = [p/tot for p in probs]
X =  stats.rv_discrete(values=(grid,probs), name='sizeDist')
print E(sqrt, X)

# but this is not ok

X = stats.poisson(3)
print E(sqrt, X)

------------------------------------------

This is the output:

11.0383463182
(7.771634331185016e-19, 1.916327339868129e-17)
(2.9999999999999996, 1.1770368678494054e-08)

So indeed, quad misses the spikes in the poisson distribution.

Is there any nice way to resolve this? Moreover, is there a better way
to build the expectation operator? Perhaps, if all works, it would be
a useful add-on to distributions.py, or else I can include it in the
distribution tutorial, if other people also think this is useful.

thanks

Nicky