  Inspired from quantum Monte Carlo, by using unbiased estimators all the time
and sampling discrete and continuous variables at the same time using
Metropolis algorithm, we present a novel, fast, and accurate high performance
Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. We named it
Randomized Parametric Expectation Maximization (RPEM). In particular, we
compared RPEM with Monolix's SAEM and Certara's QRPEM for a realistic
two-compartment Voriconazole model with ordinary differential equations (ODEs)
and using simulated data. We show that RPEM is 3 to 4 times faster than SAEM
and QRPEM, and more accurate than them in reconstructing the population
parameters.
