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 )
dataList
: List of data for each separate Dirichlet mixture objectmaxY
: 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.dpobjlist A Hierarchical Dirichlet Process object that can be fitted, plotted etc.
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