
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
A team of researchers from Oak Ridge National Laboratory (ORNL) designed, implemented, and evaluated a high-performance computing (HPC) runtime system.