marginalLikelihood_bySufficientStatistics.CatDP: Marginal likelihood of a "CatDP" object, using sufficient...

In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

Description

Generate the marginal likelihood of the following model structure:

pi|alpha \sim DP(alpha,U)

x|pi \sim Categorical(pi)

where DP(alpha,U) is a Dirichlet Process on positive integers, alpha is the "concentration parameter" of the Dirichlet Process, U is the "base measure" of this Dirichlet process, it is an uniform distribution on all positive integers.Categorical() is the Categorical distribution. See dCategorical for the definition of the Categorical distribution.
In the case of CatDP, x can only be positive integers.
The model structure and prior parameters are stored in a "CatDP" object.
Marginal likelihood = p(x|alpha).

Usage

## S3 method for class 'CatDP'
marginalLikelihood_bySufficientStatistics(obj, ss, LOG = TRUE, ...)

Arguments

obj	A "CatDP" object.
ss	Sufficient statistics of x. In Categorical-DP case the sufficient statistic of sample x can either be an object of type "ssCatDP" generated by sufficientStatistics(), or x itself(if x is a integer vector with all positive values).
LOG	Return the log density if set to "TRUE".
...	Additional arguments to be passed to other inherited types.

Value

numeric, the marginal likelihood.

See Also

CatDP, marginalLikelihood_bySufficientStatistics.CatDP

bbricks documentation built on July 8, 2020, 7:29 p.m.