DirichletProcessGaussian: Create a Dirichlet Mixture of Gaussians

In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

Create a Dirichlet Mixture of Gaussians

Description

This is the constructor function to produce a dirichletprocess object with a Gaussian mixture kernel with unknown mean and variance. The base measure is a Normal Inverse Gamma distribution that is conjugate to the posterior distribution.

Usage

DirichletProcessGaussian(y, g0Priors = c(0, 1, 1, 1), alphaPriors = c(2, 4))

Arguments

y	Data
g0Priors	Base Distribution Priors \gamma = (\mu _0, k_0 , \alpha _0 , \beta _0)
alphaPriors	Alpha prior parameters. See UpdateAlpha.

Details

G_0(\theta | \gamma) = N \left(\mu | \mu_0, \frac{\sigma^2}{k_0} \right) \mathrm{Inv-Gamma} \left(\sigma^2 | \alpha_0, \beta_0 \right)

We recommend scaling your data to zero mean and unit variance for quicker convergence.

Value

Dirichlet process object

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