دانلود گزارش کارورزی و کار آموزی

چکیده : 

The wide applicability of Gibbs sampling has increased the use of more complex and multi-level

hierarchical models. To use these models entails dealing with hyperparameters in the deeper levels of a

hierarchy. There are three typical methods for dealing with these hyperparameters: specify them, estimate

them, or use a ‘flat’ prior. Each of these strategies has its own associated problems. In this paper, using

an empirical Bayes approach, we show how the hyperparameters can be estimated in a way that is both

computationally feasible and statistically valid.