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.
DirichletProcessGaussian(y, g0Priors = c(0, 1, 1, 1), alphaPriors = c(2, 4))Dirichlet process object
Data
Base Distribution Priors \(\gamma = (\mu _0, k_0 , \alpha _0 , \beta _0)\)
Alpha prior parameters. See UpdateAlpha.
\(G_0(\theta | \gamma) = N \left(\mu | \mu_0, \frac{\sigma^2}{k_0} \right) \mathrm{Inv-Gamma} \left(\sigma^2 | \alpha_0, \beta_0 \right)\)
We recommend scaling your data to zero mean and unit variance for quicker convergence.