NormalInverseChisq(μ, σ2, κ, ν)

A Normal-χ^-2 distribution is a conjugate prior for a Normal distribution with unknown mean and variance. It has parameters:

• μ: expected mean
• σ2 > 0: expected variance
• κ ≥ 0: mean confidence
• ν ≥ 0: variance confidence

The parameters have a natural interpretation when used as a prior for a Normal distribution with unknown mean and variance: μ and σ2 are the expected mean and variance, while κ and ν are the respective degrees of confidence (expressed in "pseudocounts"). When interpretable parameters are important, this makes it a slightly more convenient parametrization of the conjugate prior.

Equivalent to a NormalInverseGamma distribution with parameters:

• m0 = μ
• v0 = 1/κ
• shape = ν/2
• scale = νσ2/2

Based on Murphy "Conjugate Bayesian analysis of the Gaussian distribution".

params(niw::NormalInverseWishart)

The parameters are

• μ::AbstractVector{T<:Real} the expected mean vector
• Λchol::Cholesky{T<:Real} the Cholesky decomposition of the scale matrix
• κ::T<:Real prior pseudocount for the mean
• ν::T<:Real prior pseudocount for the covariance