Create a Dirichlet process object using the mean and scale parameterisation of the Beta distribution bounded on \((0, maxY)\). The Pareto distribution is used as a prior on the scale parameter to ensure that the likelihood is 0 at the boundaries.
DirichletProcessBeta2(
y,
maxY,
g0Priors = 2,
alphaPrior = c(2, 4),
mhStep = c(1, 1),
verbose = TRUE,
mhDraws = 250
)Dirichlet process object
Data for which to be modelled.
End point of the data
Prior parameters of the base measure \((\gamma\).
Prior parameters for the concentration parameter. See also UpdateAlpha.
Step size for Metropolis Hastings sampling algorithm.
Logical, control the level of on screen output.
Number of Metropolis-Hastings samples to perform for each cluster update.
\(G_0 (\mu , \nu | maxY, \alpha ) = U(\mu | 0, maxY) \mathrm{Pareto} (\nu | x_m, \gamma)\).