For following model structure: Create an object of type "HDP", which represents the Dirichlet-Process model structure: G_j|gamma ~ DP(gamma,U), j = 1:J pi_j|G_j,alpha ~ DP(alpha,G_j) z|pi_j ~ Categorical(pi_j) k|z,G_j ~ Categorical(G_j), if z is a sample from the base measure G theta_k|psi ~ H0(psi) x|theta_k,k ~ 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(gamma,G_j) is a Dirichlet Process on integers with concentration parameter alpha and base measure G_j. The choice of F() and H0() can be arbitrary, they are distributions of x and theta_k correspondingly. In the case of HDP, z and k can only be positive integers. The sufficient statistics of a set of samples x in a "HDP" object is the same sufficient statistics of the "BasicBayesian" inside the "HDP", see examples.
# S3 method for HDP
sufficientStatistics_Weighted(obj, x, w, ...)A "HDP" object.
Random samples of the "BasicBayesian" object.
numeric, sample weights.
further arguments passed to the corresponding sufficientStatistics method of the "BasicBayesian" object.
Return the sufficient statistics of the corresponding BasicBayesian type, see examples.
Teh, Yee W., et al. "Sharing clusters among related groups: Hierarchical Dirichlet processes." Advances in neural information processing systems. 2005.