DirichletProcessBeta: Dirichlet process mixture of the Beta distribution.

In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

Dirichlet process mixture of the Beta distribution.

Description

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

Usage

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.

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).

Value

Dirichlet process object

dirichletprocess documentation built on Aug. 25, 2023, 5:19 p.m.