`ConjugatePriors.NormalInverseChisq`

— Type`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".

`StatsAPI.params`

— Method`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