DirichletProcessGaussian: Create a Dirichlet Mixture of Gaussians In dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

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

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

Arguments

 y Data g0Priors Base Distribution Priors γ = (μ _0, k_0 , α _0 , β _0) alphaPriors Alpha prior parameters. See UpdateAlpha.

Details

G_0(θ | γ) = N ≤ft(μ | μ_0, \frac{σ^2}{k_0} \right) \mathrm{Inv-Gamma} ≤ft(σ^2 | α_0, β_0 \right)

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

Value

Dirichlet process object

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