For the 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 where DP(gamma,U) is a Dirichlet Process on integers, gamma is the concentration parameter of the Dirichlet Process. DP(gamma,G_j) is a Dirichlet Process on integers with concentration parameter alpha and base measure G_j. In the case of CatHDP, z and k can only be positive integers. Update the prior knowledge by adding the information of newly observed samples z and k. The model structure and prior parameters are stored in a "CatHDP" object, the prior parameters in this object will be updated after running this function.
# S3 method for CatHDP
posterior(obj, ss1, ss2, j, w = NULL, ...)A "CatHDP" object.
Sufficient statistics of k. In CatHDP case the sufficient statistic of sample k is k itself(if k is a integer vector with all positive values).
Sufficient statistics of z. In CatHDP case the sufficient statistic of sample z is z itself(if z is a integer vector with all positive values).
integer, group label.
Sample weights, default NULL.
Additional arguments to be passed to other inherited types.
None. the model stored in "obj" will be updated based on "ss1" and "ss2".
Teh, Yee W., et al. "Sharing clusters among related groups: Hierarchical Dirichlet processes." Advances in neural information processing systems. 2005.