OBSMD: AN R-PACKAGE FOR OBJECTIVE BAYESIAN
MODEL DISCRIMINATION IN FOLLOW-UP DESIGNS

Marta Nai Ruscone; Laura Deldossi

OBSMD: AN R-PACKAGE FOR OBJECTIVE BAYESIAN MODEL DISCRIMINATION IN FOLLOW-UP DESIGNS

Marta Nai Ruscone
, Laura Deldossi

Risultato della ricerca: Contributo in libro › Contributo a conferenza

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.

Lingua originale	Inglese
Titolo della pubblicazione ospite	Proceedings SCo 2013
Pagine	1-4
Numero di pagine	4
Stato di pubblicazione	Pubblicato - 2013
Evento	SCo 2013 - Milano Durata: 9 set 2013 → 12 set 2013

Convegno

Convegno	SCo 2013
Città	Milano
Periodo	9/9/13 → 12/9/13

Keywords

FOLLOW-UP DESIGNS
MODEL DISCRIMINATION
objective Bayesian

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title = "OBSMD: AN R-PACKAGE FOR OBJECTIVE BAYESIAN MODEL DISCRIMINATION IN FOLLOW-UP DESIGNS",

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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author = "\{Nai Ruscone\}, Marta and Laura Deldossi",

year = "2013",

language = "English",

isbn = "97888-6493-019-0",

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AU - Nai Ruscone, Marta

AU - Deldossi, Laura

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N2 - 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.

AB - 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.

KW - FOLLOW-UP DESIGNS

KW - MODEL DISCRIMINATION

KW - objective Bayesian

KW - FOLLOW-UP DESIGNS

KW - MODEL DISCRIMINATION

KW - objective Bayesian

UR - http://hdl.handle.net/10807/53362

M3 - Conference contribution

SN - 97888-6493-019-0

SP - 1

EP - 4

BT - Proceedings SCo 2013

T2 - SCo 2013

Y2 - 9 September 2013 through 12 September 2013