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.
Nai Ruscone, M., Deldossi, L., OBSMD: AN R-PACKAGE FOR OBJECTIVE BAYESIANMODEL DISCRIMINATION IN FOLLOW-UP DESIGNS, in Proceedings SCo 2013, (Milano, 09-12 September 2013), Poliscript 2013, Milano 2013: 1-4 [http://hdl.handle.net/10807/53362]
OBSMD: AN R-PACKAGE FOR OBJECTIVE BAYESIAN MODEL DISCRIMINATION IN FOLLOW-UP DESIGNS
Nai Ruscone, Marta;Deldossi, Laura
2013
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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