dPosteriorPredictive.CatDP {bbricks}R Documentation

Posterior predictive density function of a "CatDP" object


Generate the the density value of the posterior predictive distribution of the following 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.
Posterior predictive density is p(x|alpha).


## S3 method for class 'CatDP'
dPosteriorPredictive(obj, x, LOG = TRUE, ...)



A "CatDP" object.


integer, the elements of the vector must all greater than 0, the samples of a Categorical distribution.


Return the log density if set to "TRUE".


Additional arguments to be passed to other inherited types.


A numeric vector, the posterior predictive density.


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

See Also

CatDP, dPosteriorPredictive.CatDP, marginalLikelihood.CatDP


x <- sample(1L:10L,size = 40,replace = TRUE)
obj <- CatDP()
ss <- sufficientStatistics(obj=obj,x=x)
posterior(obj = obj,ss = ss)
dPosteriorPredictive(obj = obj,x=1L:11L,LOG = FALSE)

[Package bbricks version 0.1.4 Index]