  The energy variance optimization algorithm over a fixed ensemble of
configurations in variational Monte Carlo is formally identical to a problem of
fitting data: we reexamine it from a statistical maximum-likelihood point of
view. We detect the origin of the problem of convergence that is often
encountered in practice and propose an alternative procedure for optimization
of trial wave functions in quantum Monte Carlo. We successfully test this
proposal by optimizing a trial wave function for the Helium trimer.
