# AP Statistics Curriculum 2007 Bayesian Hierarchical

### From Socr

## Contents |

## Probability and Statistics Ebook - Bayesian Hierarchical Models

Sometimes we cannot be sure about the factuality of our prior knowledge. Often we make one or more assumptions about the relationships between the different unknown parameters θ from which observations x has density p(x|θ). These associations are sometimes referred to as *structural*. In some cases the structural prior knowledge is combined with a standard form of Bayesian prior belief about the parameters of the structure. In the case where θ_{i} are independently and identically distributed, their common distribution might depend on a parameter η which we refer to as a hyperparameter. When the η is unknown we have a second tier in which we suppose to have a hyperprior p(η) expressing our beliefs about possible values of η. In such a case we may say that we have a hierarchical model.

## Idea of a Hierarchical Model

## Hierarchical Normal Model

## Stein Estimator

## Bayesian analysis for unknown overall mean

## See also

## References

- SOCR Home page: http://www.socr.ucla.edu

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