DirichletProcessBeta function

Dirichlet process mixture of the Beta distribution.

Dirichlet process mixture of the Beta distribution.

Create a Dirichlet process object using the mean and scale parameterisation of the Beta distribution bounded on (0,maxY)(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 )

Arguments

  • y: Data for which to be modelled.
  • maxY: End point of the data
  • g0Priors: Prior parameters of the base measure (α0,β0)(\alpha _0, \beta _0).
  • alphaPrior: Prior parameters for the concentration parameter. See also UpdateAlpha.
  • mhStep: Step size for Metropolis Hastings sampling algorithm.
  • hyperPriorParameters: Hyper-prior parameters for the prior distributions of the base measure parameters (a,b)(a, b).
  • 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,α0,β0)=U(μ0,maxY)InvGamma(να0,β0)G_0 (\mu , \nu | maxY, \alpha _0 , \beta _0) = U(\mu | 0, maxY) \mathrm{Inv-Gamma} (\nu | \alpha _0, \beta _0).

The parameter β0\beta _0 also has a prior distribution β0Gamma(a,b)\beta _0 \sim \mathrm{Gamma} (a, b) if the user selects Fit(...,updatePrior=TRUE).