DirichletProcessHierarchicalBeta: Create a Hierarchical Dirichlet Mixture of Beta Distributions

In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

Create a Hierarchical Dirichlet Mixture of Beta Distributions

Description

Create a Hierarchical Dirichlet Mixture of Beta Distributions

Usage

DirichletProcessHierarchicalBeta(
  dataList,
  maxY,
  priorParameters = c(2, 8),
  hyperPriorParameters = c(1, 0.125),
  gammaPriors = c(2, 4),
  alphaPriors = c(2, 4),
  mhStepSize = c(0.1, 0.1),
  numSticks = 50,
  mhDraws = 250
)

Arguments

dataList	List of data for each separate Dirichlet mixture object
maxY	Maximum value for the Beta distribution.
priorParameters	Prior Parameters for the top level base distribution.
hyperPriorParameters	Hyper prior parameters for the top level base distribution.
gammaPriors	Prior parameters for the top level concentration parameter.
alphaPriors	Prior parameters for the individual parameters.
mhStepSize	Metropolis Hastings jump size.
numSticks	Truncation level for the Stick Breaking formulation.
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.