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

Description Usage Arguments Value References See Also

View source: R/Dirichlet_Process.r


Generate the the density value of the posterior predictive distribution of the following structure:

G|gamma \sim DP(gamma,U)

pi_j|G,alpha \sim DP(alpha,G), j = 1:J

z|pi_j \sim Categorical(pi_j)

k|z,G \sim Categorical(G), \textrm{ if z is a sample from the base measure G}

theta_k|psi \sim H0(psi)

x|theta_k,k \sim F(theta_k)

where DP(gamma,U) is a Dirichlet Process on positive integers, gamma is the "concentration parameter", U is the "base measure" of this Dirichlet process, U is an uniform distribution on all positive integers. DP(alpha,G) is a Dirichlet Process on integers with concentration parameter alpha and base measure G. The choice of F() and H0() can be described by an arbitrary "BasicBayesian" object such as "GaussianGaussian","GaussianInvWishart","GaussianNIW", "GaussianNIG", "CatDirichlet", and "CatDP". See ?BasicBayesian for definition of "BasicBayesian" objects, and see for example ?GaussianGaussian for specific "BasicBayesian" instances. As a summary, An "HDP" object is simply a combination of a "CatHDP" object (see ?CatHDP) and an object of any "BasicBayesian" type.
In the case of HDP, z and k can only be positive integers.
The model structure and prior parameters are stored in a "HDP" object.
Posterior predictive density = p(x,z,k|gamma,alpha,psi) when x is not NULL, or p(z,k|gamma,alpha,psi) when x is NULL.


## S3 method for class 'HDP'
dPosteriorPredictive(obj, x = NULL, z, k, j, LOG = TRUE, ...)



A "HDP" object.


Random samples of the "BasicBayesian" object.




integer, the partition label of the parameter space where the observation x is drawn from.


integer, group label.


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

HDP, dPosteriorPredictive.HDP, marginalLikelihood.HDP

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