Create a Dirichlet process object using the mean and scale parameterisation of the Beta distribution bounded on \((0, maxY)\).
DirichletProcessBeta(
y,
maxY,
g0Priors = c(2, 8),
alphaPrior = c(2, 4),
mhStep = c(1, 1),
hyperPriorParameters = c(1, 0.125),
verbose = TRUE,
mhDraws = 250
)Dirichlet process object
Data for which to be modelled.
End point of the data
Prior parameters of the base measure \((\alpha _0, \beta _0)\).
Prior parameters for the concentration parameter. See also UpdateAlpha.
Step size for Metropolis Hastings sampling algorithm.
Hyper-prior parameters for the prior distributions of the base measure parameters \((a, b)\).
Logical, control the level of on screen output.
Number of Metropolis-Hastings samples to perform for each cluster update.
\(G_0 (\mu , \nu | maxY, \alpha _0 , \beta _0) = U(\mu | 0, maxY) \mathrm{Inv-Gamma} (\nu | \alpha _0, \beta _0)\).
The parameter \(\beta _0\) also has a prior distribution \(\beta _0 \sim \mathrm{Gamma} (a, b)\) if the user selects Fit(...,updatePrior=TRUE).