bbricks (version 0.1.4)

dPosteriorPredictive.CatDirichlet: Posterior predictive density function of a "CatDirichlet" object

Description

Generate the the density value of 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 CatDirichlet
dPosteriorPredictive(obj, x, LOG = TRUE, ...)

Arguments

obj

A "CatDirichlet" object.

x

numeric/integer/character vector, observed Categorical samples.

LOG

Return the log density if set to "TRUE".

...

Additional arguments to be passed to other inherited types.

Value

A numeric vector, the posterior predictive density.

References

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

See Also

CatDirichlet, dPosteriorPredictive.CatDirichlet, marginalLikelihood.CatDirichlet

Examples

# NOT RUN {
obj <- CatDirichlet(gamma=list(alpha=runif(26,1,2),uniqueLabels = letters))
x <- sample(letters,size = 20,replace = TRUE)
## res1 and res2 should provide the same result
res1 <- dPosteriorPredictive(obj = obj,x=x,LOG = TRUE)
res2 <- numeric(length(x))
for(i in seq_along(x)) res2[i] <- marginalLikelihood(obj=obj,x=x[i],LOG = TRUE)
# }