R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework

Laura Deldossi, Marta Nai Ruscone

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

1 Citazioni (Scopus)


Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the active factors. The R package OBsMD solves this problem by implementing the objective Bayesian methodology proposed by Consonni and Deldossi (2016). The main feature of this approach is that the follow-up designs are obtained through the use of just two functions, OBsProb() and OMD() without requiring any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for conducting a design of experiments to solve real world problems.
Lingua originaleEnglish
pagine (da-a)1-37
Numero di pagine37
RivistaJournal of Statistical Software
Stato di pubblicazionePubblicato - 2020


  • Bayesian design of experiments
  • Bayesian model selection
  • Model discrimination
  • Screening experiments


Entra nei temi di ricerca di 'R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework'. Insieme formano una fingerprint unica.

Cita questo