RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm

Abstract

Inspired from quantum Monte Carlo methods, we developed a novel, fast, accurate, robust, and generalizable high performance algorithm for Monte Carlo Parametric Expectation Maximization (MCPEM) methods. We named it Randomized Parametric Expectation Maximization (RPEM). RPEM can be used on a personal computer as an independent engine or can serve as a 'booster' to be combined with MCPEM engines used in current population modeling software tools. RPEM can also run on supercomputer clusters, since it is fully parallelized and scalable.

NPSA: Nonparametric Simulated Annealing for Global Optimization

“…For example, by using Eq. ( 3), each of the optimal w k can be obtained from the solution of the iteration relation [41],…”

“…In this section, we use the model and the data file which are the same as in those described in detail in our novel Monte Carlo parametric expectation maximization algorithm paper 'RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm' [41]. We use a Voriconazole model [13,14,34] and we follow the data and model format for Pmetrics [13].…”