dPosteriorPredictive.HDP2 {bbricks}R Documentation

Posterior predictive density function of a "HDP2" object


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

G |eta \sim DP(eta,U)

G_m|gamma,G \sim DP(gamma,G), m = 1:M

pi_{mj}|G_m,alpha \sim DP(alpha,G_m), j = 1:J_m

z|pi_{mj} \sim Categorical(pi_{mj})

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

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

theta_u|psi \sim H0(psi)

x|theta_u,u \sim F(theta_u)

where DP(eta,U) is a Dirichlet Process on positive integers, eta is the "concentration parameter", U is the "base measure" of this Dirichlet process, U is an uniform distribution on all positive integers. DP(gamma,G) is a Dirichlet Process on integers with concentration parameter gamma and base measure G. DP(alpha,G_m) is a Dirichlet Process on integers with concentration parameter alpha and base measure G_m. 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 "HDP2" object is simply a combination of a "CatHDP2" object (see ?CatHDP2) and an object of any "BasicBayesian" type.
In the case of HDP2, u, z and k can only be positive integers.
The model structure and prior parameters are stored in a "HDP2" object.
Posterior predictive density = p(u,z,k,x|eta,gamma,alpha,psi) when x is not NULL, or p(u,z,k|eta,gamma,alpha,psi) when x is NULL.


## S3 method for class 'HDP2'
dPosteriorPredictive(obj, x = NULL, u, k, z, m, j, LOG = TRUE, ...)



A "HDP2" 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.


integer, subgroup 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

HDP2, dPosteriorPredictive.HDP2, marginalLikelihood.HDP2

[Package bbricks version 0.1.4 Index]