Bayesian optimal designs for discriminating between non-Normal models
Designs are found for discriminating between two non-Normal models in the presence of prior information. The KL-optimality criterion, where the true model is assumed to be completely known, is extended to a criterion where prior distributions of the parameters and a prior probability of each model to be true are assumed. Concavity of this criterion is proved. Thus, the results of optimal design theory apply in this context and optimal designs can be constructed and checked by the General Equivalence Theorem. Some illustrative examples are provided.
|Date of creation:||08 May 2007|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +39 02 503 16486
Fax: +39 02 503 16475
Web page: http://services.bepress.com/unimi
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:bep:unimip:unimi-1055. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.