DirichletProcessHierarchicalBeta function

Create a Hierarchical Dirichlet Mixture of Beta Distributions

Create a Hierarchical Dirichlet Mixture of Beta Distributions

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.

Returns

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