Add to ORKG23 pages, 1 article*Default Priors for Robust Bayesian Inference* (Casella, George; Wells, Martin T.) 23 page
Abstract: Many scientific problems have unknown parameters that are thought to lie in some known set...
Reference analysis is an objective Bayesian approach to finding noninformative prior distributions. ...
textabstractA sensible Bayesian model selection or comparison strategy implies selecting the model w...
Robust Bayesian inference involves examining the performance of Bayes rules from a class of prior di...
We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inf...
The reference priors, initiated in Bernardo (1979) and further developed in Berger and Bernardo (199...
Partial prior information on the marginal distribution of an observable random variable is considere...
This issue was undated. The date given is an estimate.21 pages, 1 article*Gibbs Sampling with Diffus...
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast l...
Especially when facing reliability data with limited information (e.g., a small number of failures),...
Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables resea...
In Chapter 2, the robustness of Bayes analysis with reference to conjugate prior classes is discusse...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98184/1/s15516709cog1903_4.pd
Beta distributions with both parameters equal to 0, ½, or 1 are the usual choices for “noninformativ...
In a Bayesian analysis the statistician must specify prior densities for the model parameters. If he...
Abstract: Many scientific problems have unknown parameters that are thought to lie in some known set...
Reference analysis is an objective Bayesian approach to finding noninformative prior distributions. ...
textabstractA sensible Bayesian model selection or comparison strategy implies selecting the model w...
Robust Bayesian inference involves examining the performance of Bayes rules from a class of prior di...
We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inf...
The reference priors, initiated in Bernardo (1979) and further developed in Berger and Bernardo (199...
Partial prior information on the marginal distribution of an observable random variable is considere...
This issue was undated. The date given is an estimate.21 pages, 1 article*Gibbs Sampling with Diffus...
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast l...
Especially when facing reliability data with limited information (e.g., a small number of failures),...
Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables resea...
In Chapter 2, the robustness of Bayes analysis with reference to conjugate prior classes is discusse...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98184/1/s15516709cog1903_4.pd
Beta distributions with both parameters equal to 0, ½, or 1 are the usual choices for “noninformativ...
In a Bayesian analysis the statistician must specify prior densities for the model parameters. If he...
Abstract: Many scientific problems have unknown parameters that are thought to lie in some known set...
Reference analysis is an objective Bayesian approach to finding noninformative prior distributions. ...
textabstractA sensible Bayesian model selection or comparison strategy implies selecting the model w...