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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings SCo 2013
Pages	1-4
Number of pages	4
Publication status	Published - 2013
Event	SCo 2013 - Milano Duration: 9 Sept 2013 → 12 Sept 2013

Conference

Conference	SCo 2013
City	Milano
Period	9/9/13 → 12/9/13

Keywords

FOLLOW-UP DESIGNS
MODEL DISCRIMINATION
objective Bayesian

Cite this

@inproceedings{57b89e3e748449539949986f0d06a136,

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.",

keywords = "FOLLOW-UP DESIGNS, MODEL DISCRIMINATION, objective Bayesian, FOLLOW-UP DESIGNS, MODEL DISCRIMINATION, objective Bayesian",

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

year = "2013",

language = "English",

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

pages = "1--4",

booktitle = "Proceedings SCo 2013",

note = "SCo 2013 ; Conference date: 09-09-2013 Through 12-09-2013",

}

TY - GEN

T1 - OBSMD: AN R-PACKAGE FOR OBJECTIVE BAYESIAN MODEL DISCRIMINATION IN FOLLOW-UP DESIGNS

AU - Nai Ruscone, Marta

AU - Deldossi, Laura

PY - 2013

Y1 - 2013

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

ER -

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

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this