Create a Dirichlet Mixture of Gaussians
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.
dirichletprocess
DirichletProcessGaussian(y, g0Priors = c(0, 1, 1, 1), alphaPriors = c(2, 4))
y
g0Priors
alphaPriors
UpdateAlpha
Dirichlet process object
G0(θ∣γ)=N(μ∣μ0,σ2k0)Inv−Gamma(σ2∣α0,β0)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)G0(θ∣γ)=N(μ∣μ0,k0σ2)Inv−Gamma(σ2∣α0,β0)
We recommend scaling your data to zero mean and unit variance for quicker convergence.
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