Hierarchical Bayes model


Hierarchical Bayes model

The hierarchical Bayes method is one of the most important topics in modern Bayesian analysis. It is a powerful tool for expressing rich statistical models that more fully reflect a given problem than a simpler model could.

Given data x,! and parameters vartheta, a simple Bayesian analysis starts with a prior probability ("prior") p(vartheta) and likelihood p(x|vartheta) to compute a posterior probability p(vartheta|x) propto p(x|vartheta)p(vartheta).

Often the prior on vartheta depends in turn on other parameters varphi that are not mentioned in the likelihood. So, the prior p(vartheta) must be replaced by a prior p(vartheta|varphi), and a prior p(varphi) on the newly introduced parameters varphi is required, resulting in a posterior probability

:p(vartheta,varphi|x) propto p(x|vartheta)p(vartheta|varphi)p(varphi).

This is the simplest example of a "hierarchical Bayes model".

The process may be repeated; For example, the parameters varphi may depend in turn on additional parameters psi,!, which will require their own prior. Eventually the process must terminate, with priors that do not depend on any other unmentioned parameters.

Examples

Suppose we have measured n,! quantities x_i, i=1,dots,n,!, where the observed data x_i,! have been measured with normally distributed errors of known standard deviation sigma,!, e.g.,

:x_i sim N(vartheta_i, sigma^2)

Suppose we are interested in estimating the vartheta_i. An approach would be to estimate the vartheta_i using a maximum likelihood approach; since the observations are independent, the likelihood factorizes and the maximum likelihood estimate is simply

:vartheta_i = x_i

However, if the quantities are related, so that for example we may think that the individual vartheta_i have themselves been drawn from an underlying distribution, then this relationship destroys the independence and suggests a more complex model, e.g.,

:x_i sim N(vartheta_i,sigma^2),:vartheta_isim N(varphi, au^2)

with improper priors varphisimflat, ausimflat in (0,infty). When nge 3, this is an identified model, and the posterior distributions of the individual vartheta_i will tend to move, or "shrink" away from the maximum likelihood estimates towards their common mean. This "shrinkage" is a typical behavior in hierarchical Bayes models.

"More examples needed."

Restrictions on priors

Some care is needed when choosing priors in a hierarchical model, particularly on scale variables at higher levels of the hierarchy such as the variable au,! in the example. The usual priors such as the Jeffreys prior often do not work, because the posterior distribution will be improper (not normalizable), and estimates made by minimizing the expected loss will be inadmissible.

"This section needs significant expansion."

Representation by directed acyclic graphs (DAGs)

A useful graphical tool for representing hierarchical Bayes models is the directed acyclic graph, or DAG. In this diagram, the likelihood function is represented as the root of the graph; each prior is represented as a separate node pointing to the node that depends on it. In a simple Bayesian model, the data x are at the root of the diagram, representing the likelihood p(x|vartheta), and the variable vartheta is placed in a node that points to the root, as in the following diagram:

: vartheta { ightarrow} x

:"Better would be a figure, but this will do for the time being"

In the simplest hierarchical Bayes model, where vartheta in turn depends on a new variable varphi, a new node labelled varphi is indicated, with an arrow pointed towards the node vartheta. See also Bayesian networks.

: varphi { ightarrow} vartheta { ightarrow} x

:"Better would be a figure, but this will do for the time being"

"Significant expansion required."

References

*Gelman, A., "et al." (2004), "Bayesian Data Analysis", Second Edition. Boca Raton: Chapman & Hall/CRC. Chapter 5.

External links

* [http://www.biomedcentral.com/1471-2105/7/514/abstract A hierarchical Bayes Model for handling sample heterogeneity in classification problems] , provides a classification model taking into consideration the uncertainty associated with measuring replicate samples.

* [http://www.labmedinfo.org/download/lmi339.pdf Hierarchical Naive Bayes Model for handling sample uncertainty] , shows how to perform classification and learning with continuous and discrete variables with replicated measurements.


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