# DirichletProcessBeta2: Dirichlet process mixture of Beta distributions with a... In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

## 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

 1 2 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 (γ. 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 (μ , ν | maxY, α ) = U(μ | 0, maxY) \mathrm{Pareto} (ν | x_m, γ).

## Value

Dirichlet process object

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