Blurb:: Rate of convergence of mean estimator within multilevel polynomial chaos

Description:: 
Multilevel Monte Carlo performs optimal resource allocation based on a
known estimator variance for the mean statistic:

\f[ Var[\hat{Q}] = \frac{\sigma^2_Q}{N} \f]

Replacing the simple ensemble average estimator in Monte Carlo with a
polynomial chaos estimator results in a different and unknown
relationship between the estimator variance and the number of samples.
In one approach to multilevel PCE, we can employ a parameterized
estimator variance:

\f[ Var[\hat{Q}] = \frac{\sigma^2_Q}{\gamma N^\kappa} \f]

for free parameters \f$\gamma\f$ and \f$\kappa\f$. 

The default values are \f$\gamma = 1\f$ and \f$\kappa = 2\f$ (adopts a
more aggressive sample profile by assuming a faster convergence rate
than Monte Carlo).  This advanced specification option allows to user
to specify \f$\kappa\f$, overriding the default.

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