DirichletProcessBeta2 function

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

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.

Returns

Dirichlet process object

Details

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