6.3.9.5.2. statsmodels.sandbox.distributions.mv_normal.expect_mc¶

statsmodels.sandbox.distributions.mv_normal.expect_mc(dist, func=<function <lambda>>, size=50000)[source]¶

calculate expected value of function by Monte Carlo integration

Parameters:	dist : distribution instance needs to have rvs defined as a method for drawing random numbers func : callable function for which expectation is calculated, this function needs to be vectorized, integration is over axis=0 size : int number of random samples to use in the Monte Carlo integration,
Returns:	expected value : ndarray return of function func integrated over axis=0 by MonteCarlo, this will have the same shape as the return of func without axis=0

Notes

this doesn’t batch

Examples

integrate probability that both observations are negative

>>> mvn = mve.MVNormal([0,0],2.)
>>> mve.expect_mc(mvn, lambda x: (x<np.array([0,0])).all(-1), size=100000)
0.25306000000000001

get tail probabilities of marginal distribution (should be 0.1)

>>> c = stats.norm.isf(0.05, scale=np.sqrt(2.))
>>> expect_mc(mvn, lambda x: (np.abs(x)>np.array([c, c])), size=100000)
array([ 0.09969,  0.0986 ])

or calling the method

>>> mvn.expect_mc(lambda x: (np.abs(x)>np.array([c, c])), size=100000)
array([ 0.09937,  0.10075])