Distributions

We proved here the list of distribution implemented by the package.

Multivariate Normal distribution

Likelihood:

\[\mathcal{N}(\mu, \Sigma) = \frac{1}{(2\pi)^{\frac{D}{2}} | \Sigma |^{\frac{1}{2}}} \exp \big\{ -\frac{1}{2} (x - \mu)^\top \Sigma^{-1} (x - \mu) \big\}\]

Terms of the canonical form:

\[\begin{aligned} \eta &= \begin{bmatrix} \Sigma^{-1} \mu \\ -\frac{1}{2}\text{vec}(\text{diag}(\Sigma^{-1})) \\ -\text{vec}(\text{tril}(\Sigma^{-1})) \end{bmatrix} \\ T(x) &= \begin{bmatrix} x \\ \text{vec}(\text{diag}(xx^\top)) \\ \text{vec}(\text{tril}(xx^\top)) \end{bmatrix} \\ A(\eta) &= \frac{1}{2} \ln |\Sigma| + \frac{1}{2} \mu^\top \Sigma^{-1}\mu \\ B(x) &= -\frac{D}{2} \ln 2\pi \\ \nabla_{\eta} A(\eta) &= \begin{bmatrix} \mu \\ \text{vec}(\Sigma + \mu \mu^\top) \end{bmatrix} \end{aligned}\]

where $\text{tril}$ is a function that returns the lower-triangular part of a matrix (diagonal not included).

ExpFamilyDistributions.Normal — Type

mutable struct Normal{D} <: AbstractNormal{D}
    param::P where P <: AbstractParameter
end

Normal distribution with full covariance matrix.

Constructors

Normal(μ, Σ)

where μ is the mean and Σ is the covariance matrix.

Examples

julia> Normal([1.0, 1.0], [2.0 0.5; 0.5 1.0])
Normal{2}:
  μ = [0.9999999999999998, 1.0]
  Σ = [2.0 0.5; 0.5 1.0]

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ExpFamilyDistributions.NormalDiag — Type

mutable struct NormalDiag{D} <: AbstractNormalDiag{D}
    param::P where P <: AbstractParameter
end

Normal distribution with a diagonal covariance matrix.

Constructors

NormalDiag(μ, v)

where μ is the mean v is the diagonal of the covariance matrix.

Examples

julia> NormalDiag([1.0, 1.0], [2.0, 1.0])
NormalDiag{2}:
  μ = [1.0, 1.0]
  Σ = [2.0 0.0; 0.0 1.0]

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Gamma distribution

Likelihood:

\[\mathcal{G}(x | \alpha, \beta) = \frac{1}{\Gamma (\alpha)}\beta^{\alpha} x^{\alpha - 1} \exp \{ -\beta x \}\]

Terms of the canonical form:

\[\begin{aligned} \eta &= \begin{bmatrix} -\beta \\ \alpha \end{bmatrix} \\ T(x) &= \begin{bmatrix} x \\ \ln x \end{bmatrix} \\ A(\eta) &= \ln \Gamma(\alpha) - \alpha \ln \beta \\ B(x) &= -\ln x \\ \nabla_{\eta} A(\eta) &= \begin{bmatrix} \frac{\alpha}{\beta} \\ \psi(\alpha) - \ln\beta \end{bmatrix} \end{aligned}\]

Note

In practice, the Gamma structure in the package represents the distribution of D independent Gamma distributed variables.

ExpFamilyDistributions.Gamma — Type

mutable struct Gamma{D} <: AbstractGamma
    param::P where P <: AbstractParameter
end

Set of D independent Gamma distributions.

Constructors

Gamma(α, β)

where α and β are the shape and rate parameters of the distribution.

Examples

julia> Gamma([1.0, 1.0], [2.0, 2.0])
Gamma{2}:
  α = [1.0, 1.0]
  β = [2.0, 2.0]

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Dirichlet distribution

Likelihood:

\[\mathcal{D}(x | \alpha) = \frac{\Gamma(\sum_{i=1}^D \alpha_i)}{\prod_{i=1}^{D}\Gamma (\alpha_i)} \prod_{i=1}^D x_i^{\alpha - 1}\]

Terms of the canonical form:

\[\begin{aligned} \eta &= \alpha \\ T(x) &= \ln x \\ A(\eta) &= \sum_{i=1}^D \ln \Gamma(\alpha_i) - \ln \Gamma(\sum_{i=1}^D \alpha_i) \\ B(x) &= -\ln x \\ \nabla_{\eta} A(\eta) &= \begin{bmatrix} \psi(\alpha_1) - \psi(\sum_{i=1}^D \alpha_i) \\ \vdots \\ \psi(\alpha_D) - \psi(\sum_{i=1}^D \alpha_i) \\ \end{bmatrix} \end{aligned}\]

ExpFamilyDistributions.Dirichlet — Type

mutable struct Dirichlet{D} <: AbstractDirichlet{D}
    param::P where P <: AbstractParameter
end

Dirichlet distribution.

Constructors

Dirichlet(α)

where α is a vector of concentrations.

Examples

julia> Dirichlet([1.0, 2.0, 3.0])
Dirichlet{3}:
  α = [1.0, 2.0, 3.0]

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Wishart distribution

Likelihood:

\[\mathcal{W}(X | W, v) = B(W, v)|X|^{\frac{(v-D-1)}{2}} \exp \bigg\{ -\frac{1}{2} \text{tr}(W^{-1}X) \bigg\} \\ B(W,v) = |W|^{-\frac{v}{2}}\bigg( 2^{\frac{vD}{2}} \pi^{\frac{D(D-1)}{4}} \prod_{i=1}^D \Gamma \big( \frac{v+1-i}{2} \big) \bigg)^{-1}\]

where $X$ and $W$ are $D \times D$ symmetric positive definite matrices.

Terms of the canonical form:

\[\begin{aligned} \eta &= \begin{bmatrix} \text{vec}(-\frac{1}{2} W^{-1}) \\ \frac{v}{2} \end{bmatrix}\\ T(x) &= \begin{bmatrix} \text{vec}(\text{diag}(X)) \\ \text{vec}(\text{tril}(X)) \\ \ln |X| \end{bmatrix} \\ A(\eta) &= \frac{v}{2} \ln |W| + \frac{vD}{2} \ln 2 + \sum_{i=1}^D \ln \Gamma \big( \frac{v+1-i}{2} \big) \\ B(x) &= -\frac{(D-1)}{2} \ln |X| - \frac{D(D-1)}{4} \ln \pi \\ \nabla_{\eta} A(\eta) &= \begin{bmatrix} \text{vec}(vW) \\ \ln |W| + D \ln 2 + \sum_{i=1}^D \psi \big( \frac{v+1-i}{2} \big) \end{bmatrix} \end{aligned}\]

ExpFamilyDistributions.Wishart — Type

mutable struct Wishart{D} <: AbstractWishart{D}
    param::P where P <: AbstractParameter
end

Wishart distribution.

Constructors

Wishart{D}()
Wishart(W[, v])

where T is the encoding type of the parameters and W is a positive definite DxD matrix.

Examples

julia> Wishart([1 0.5; 0.5 1], 2)
Wishart{2}:
  W = [0.9999999999999997 0.5; 0.5 1.0]
  v = 2.0

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