DirichletProcessBeta function

Dirichlet process mixture of the Beta distribution.

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

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 $(\alpha _0, \beta _0)$.
• 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 $(a, b)$.
• 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

$G_0 (\mu , \nu | maxY, \alpha _0 , \beta _0) = U(\mu | 0, maxY) \mathrm{Inv-Gamma} (\nu | \alpha _0, \beta _0)$.

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