1 citations · 1 across the 5 of their papers we have counts for
5 papers
Uncertainty Quantification Via the Posterior Predictive Variance
Sanjay Chaudhuri, Dean Dustin, Bertrand Clarke
We use the law of total variance to generate multiple expansions for the posterior predictive variance. These expansions are sums of terms involving conditional expectations and co…
A conservation law for posterior predictive variance
Bertrand Clarke, Dean Dustin
We use the law of total variance to generate multiple expressions for the posterior predictive variance in Bayesian hierarchical models. These expressions are sums of terms involvi…
Post-model-selection prediction for GLM's
Dean Dustin, Bertrand Clarke
We give two prediction intervals (PI) for Generalized Linear Models that take model selection uncertainty into account. The first is a straightforward extension of asymptotic norma…
Testing for the Important Components of Posterior Predictive Variance
Dean Dustin, Bertrand Clarke
We give a decomposition of the posterior predictive variance using the law of total variance and conditioning on a finite dimensional discrete random variable. This random variable…
Predictive Criteria for Prior Selection Using Shrinkage in Linear Models
Dean Dustin, Bertrand Clarke, Jennifer Clarke
Choosing a shrinkage method can be done by selecting a penalty from a list of pre-specified penalties or by constructing a penalty based on the data. If a list of penalties for a c…