DirichletProcessBeta: Dirichlet process mixture of the Beta distribution.

Description Usage Arguments Details Value

View source: R/dirichlet_process_beta.R

Description

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

Usage

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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 (α _0, β _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 (μ , ν | maxY, α _0 , β _0) = U(μ | 0, maxY) \mathrm{Inv-Gamma} (ν | α _0, β _0).

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

Value

Dirichlet process object


dirichletprocess documentation built on July 2, 2020, 2 a.m.