Add to ORKGmarginal likelihood estimation In ML model selection we judge models by their ML score and the number of parameters. In Bayesian context we: • Use model averaging if we can “jump ” between models (reversible jum
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
The problem of evaluating the goodness of the predictive distributions devel-oped by the Bayesian mo...
A multi-level model allows the possibility of marginalization across levels in different ways, yield...
Model selection methods provide a way to select one model among a set of models in a statistically v...
The standard methodology when building statistical models has been to use one of several algorithms ...
A model averaged estimator is composed of estimators, each obtained from a different model, that are...
2005 Joint Annual Meeting of the Interface and the Classification Society of North America, St. Loui...
Many statistical scenarios initially involve several candidate models that describe the data-generat...
This paper investigates the performance of the predictive distributions of Bayesian models. To overc...
The standard practice of selecting a single model from some class of models and then making inferenc...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Abstract. The traditional use of model selection methods in practice is to proceed as if the final s...
The traditional use of model selection methods in practice is to proceed as if the final selected mo...
A data-driven method for frequentist model averaging weight choice is developed for gen-eral likelih...
A data-driven method for frequentist model averaging weight choice is developed for general likeliho...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
The problem of evaluating the goodness of the predictive distributions devel-oped by the Bayesian mo...
A multi-level model allows the possibility of marginalization across levels in different ways, yield...
Model selection methods provide a way to select one model among a set of models in a statistically v...
The standard methodology when building statistical models has been to use one of several algorithms ...
A model averaged estimator is composed of estimators, each obtained from a different model, that are...
2005 Joint Annual Meeting of the Interface and the Classification Society of North America, St. Loui...
Many statistical scenarios initially involve several candidate models that describe the data-generat...
This paper investigates the performance of the predictive distributions of Bayesian models. To overc...
The standard practice of selecting a single model from some class of models and then making inferenc...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Abstract. The traditional use of model selection methods in practice is to proceed as if the final s...
The traditional use of model selection methods in practice is to proceed as if the final selected mo...
A data-driven method for frequentist model averaging weight choice is developed for gen-eral likelih...
A data-driven method for frequentist model averaging weight choice is developed for general likeliho...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
The problem of evaluating the goodness of the predictive distributions devel-oped by the Bayesian mo...
A multi-level model allows the possibility of marginalization across levels in different ways, yield...