DirichletProcessBeta2: Dirichlet process mixture of Beta distributions with a...

In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

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

Description

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.

Usage

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.

Details

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

Value

Dirichlet process object

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