DirichletProcessBeta2 function

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

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)(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 (γ(\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

G0(μ,νmaxY,α)=U(μ0,maxY)Pareto(νxm,γ)G_0 (\mu , \nu | maxY, \alpha ) = U(\mu | 0, maxY) \mathrm{Pareto} (\nu | x_m, \gamma).