rPosteriorPredictive.CatDirichlet: Generate random samples from the posterior predictive...

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

Description

Generate random samples from the posterior predictive distribution of the following 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
posterior predictive is a distribution of x|alpha

Usage

## S3 method for class 'CatDirichlet'
rPosteriorPredictive(obj, n, ...)

Arguments

obj	A "CatDirichlet" object.
n	integer, number of samples.
...	Additional arguments to be passed to other inherited types.

Value

A vector of the same type as obj$gamma$uniqueLabels.

References

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

See Also

CatDirichlet, dPosteriorPredictive.CatDirichlet

Examples

obj <- CatDirichlet(gamma=list(alpha=runif(26,1,2),uniqueLabels = letters))
rPosteriorPredictive(obj=obj,n=200)

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