# rPosteriorPredictive.CatHDP2: Generate random samples from the posterior predictive... In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

## Description

Generate random samples from the posterior predictive distribution of the following structure:

G |eta \sim DP(eta,U)

G_m|gamma \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_m

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

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. Categorical() is the Categorical distribution. See `dCategorical` for the definition of the Categorical distribution.
In the case of CatHDP2, u, z and k can only be positive integers.
The model structure and prior parameters are stored in a "CatHDP2" object.
Posterior predictive is a distribution of u,z,k|alpha,gamm,eta,U.

## Usage

 ```1 2``` ```## S3 method for class 'CatHDP2' rPosteriorPredictive(obj, n = 1L, m, j, ...) ```

## Arguments

 `obj` A "CatHDP2" object. `n` integer, number of samples. `m` integer, group label. `j` integer, subgroup label. `...` 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.

`CatHDP2`, `dPosteriorPredictive.CatHDP2`