Dirichlet process mixture of Beta distributions with a Uniform Pareto base measure.

Source: R/dirichlet_process_beta_2.R

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)

Arguments

y

Data for which to be modelled.
maxY

End point of the data
g0Priors

Prior parameters of the base measure \((\gamma\).
alphaPrior

Prior parameters for the concentration parameter. See also UpdateAlpha.
mhStep

Step size for Metropolis Hastings sampling algorithm.
verbose

Logical, control the level of on screen output.
mhDraws

Number of Metropolis-Hastings samples to perform for each cluster update.

Value

Dirichlet process object

Details

\(G_0 (\mu , \nu | maxY, \alpha ) = U(\mu | 0, maxY) \mathrm{Pareto} (\nu | x_m, \gamma)\).