Dirichlet process mixture of the Beta distribution.
Create a Dirichlet process object using the mean and scale parameterisation of the Beta distribution bounded on .
DirichletProcessBeta( y, maxY, g0Priors = c(2, 8), alphaPrior = c(2, 4), mhStep = c(1, 1), hyperPriorParameters = c(1, 0.125), verbose = TRUE, mhDraws = 250 )
y
: Data for which to be modelled.maxY
: End point of the datag0Priors
: Prior parameters of the base measure .alphaPrior
: Prior parameters for the concentration parameter. See also UpdateAlpha
.mhStep
: Step size for Metropolis Hastings sampling algorithm.hyperPriorParameters
: Hyper-prior parameters for the prior distributions of the base measure parameters .verbose
: Logical, control the level of on screen output.mhDraws
: Number of Metropolis-Hastings samples to perform for each cluster update.Dirichlet process object
.
The parameter also has a prior distribution if the user selects Fit(...,updatePrior=TRUE)
.
Useful links