DirichletProcessHierarchicalMvnormal2: Create a Hierarchical Dirichlet Mixture of semi-conjugate...

In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

Create a Hierarchical Dirichlet Mixture of semi-conjugate Multivariate Normal Distributions

Description

Create a Hierarchical Dirichlet Mixture of semi-conjugate Multivariate Normal Distributions

Usage

DirichletProcessHierarchicalMvnormal2(
  dataList,
  g0Priors,
  gammaPriors = c(2, 4),
  alphaPriors = c(2, 4),
  numSticks = 50,
  numInitialClusters = 1,
  mhDraws = 250
)

Arguments

dataList	List of data for each separate Dirichlet mixture object
g0Priors	Prior Parameters for the top level base distribution.
gammaPriors	Prior parameters for the top level concentration parameter.
alphaPriors	Prior parameters for the individual parameters.
numSticks	Truncation level for the Stick Breaking formulation.
numInitialClusters	Number of clusters to initialise with.
mhDraws	Number of Metropolis-Hastings samples to perform for each cluster update.

Value

dpobjlist A Hierarchical Dirichlet Process object that can be fitted, plotted etc.

dirichletprocess documentation built on Aug. 25, 2023, 5:19 p.m.