bbricks (version 0.1.1)

rPosteriorPredictive.CatDP: Posterior predictive random generation of a "CatDP" object

Description

Generate random samples from the posterior predictive distribution of the following structure: pi|alpha ~ DP(alpha,U) x|pi ~ 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. In the case of CatDP, x can only be positive integers. The model structure and prior parameters are stored in a "CatDP" object. Posterior predictive is a distribution of x|alpha.

Usage

# S3 method for CatDP
rPosteriorPredictive(obj, n = 1L, ...)

Arguments

obj

A "CatDP" object.

n

integer, number of samples.

...

Additional arguments to be passed to other inherited types.

Value

integer, the categorical samples.

References

Teh, Yee W., et al. "Sharing clusters among related groups: Hierarchical Dirichlet processes." Advances in neural information processing systems. 2005.

See Also

@seealso CatDP, dPosteriorPredictive.CatDP

Examples

# NOT RUN {
x <- sample(1L:10L,size = 40,replace = TRUE)
obj <- CatDP()
ss <- sufficientStatistics(obj=obj,x=x)
posterior(obj = obj,ss = ss)
rPosteriorPredictive(obj = obj,n=200L)
# }