Occasionally screening designs do not lead to unequivocal conclusions regarding which combinations of factors (models) are active. From a Bayesian viewpoint, this means that the posterior distribution on model space will be fairly evenly spread out over a few models. In these circumstances, a follow-up design is needed, the aim being of choosing extra runs in order to solve, or at least alleviate, this ambiguity. This R-package OBsMD implements the objective Bayesian methodology developed in Consonni and Deldossi (2013) for this scope.
OBsMD: an R package for objective Bayesian model discrimination in follow-up design
Nai Ruscone, Marta
2013-01-01
Abstract
Occasionally screening designs do not lead to unequivocal conclusions regarding which combinations of factors (models) are active. From a Bayesian viewpoint, this means that the posterior distribution on model space will be fairly evenly spread out over a few models. In these circumstances, a follow-up design is needed, the aim being of choosing extra runs in order to solve, or at least alleviate, this ambiguity. This R-package OBsMD implements the objective Bayesian methodology developed in Consonni and Deldossi (2013) for this scope.
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