marginalLikelihood_bySufficientStatistics.CatDirichlet: Marginal likelihood of a "CatDirichlet" object, using...

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

Description

Generate the marginal likelihood of a set of observations of the following model structure:

pi|alpha \sim Dir(alpha)

x|pi \sim Categorical(pi)

Where Dir() is the Dirichlet distribution, Categorical() is the Categorical distribution. See ?dDir and dCategorical for the definitions of these distribution.
The model structure and prior parameters are stored in a "CatDirichlet" object.
Marginal likelihood is the likelihood of x|alpha

Usage

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

Arguments

obj	A "CatDirichlet" object.
ss	Sufficient statistics of x. In Categorical-Dirichlet case the sufficient statistic of sample x can be either x itself, of an "ssCat" object generated by the function sufficientStatistics.CatDirichlet().
LOG	Return the log density if set to "TRUE".
...	Additional arguments to be passed to other inherited types.

Value

numeric, the marginal likelihood.

References

Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.

See Also

CatDirichlet, marginalLikelihood.CatDirichlet

Examples

obj <- CatDirichlet(gamma=list(alpha=runif(26,1,2),uniqueLabels = letters))
x <- sample(letters,size = 20,replace = TRUE)
marginalLikelihood(obj=obj,x=x,LOG = TRUE) #marginal likelihood
ss <- sufficientStatistics(obj = obj,x=x)
marginalLikelihood_bySufficientStatistics(obj=obj,ss = ss,LOG = TRUE)

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