ClosedFormExpectations.LinearLogGammaType
LinearLogGamma(α, β, weights)

An unnormalized multivariate distribution derived from the LogGamma distribution. (see LogGamma)

The LinearLogGamma distribution is an distribution on a multidimensional x, derived from the LogGamma distribution. It is defined as:

$$$LLG(x | lpha, eta, w) = LG(x^T w | a, b),$$$

where weights is a fixed vector of covariates, and lpha and eta are the scale and shape parameters of the LogGamma distribution, respectively. Fields

α::T: The scale parameter of the LogGamma distribution.

β::T: The shape parameter of the LogGamma distribution.

weights::C: The fixed vector of covariates.

ClosedFormExpectations.LogGammaType
LogGamma(α, β)

The LogGamma distribution is a continuous probability distribution on the real numbers. It is defined as:

The probability density function of the LogGamma distribution is defined as:

math
\mathcal{LG}(x \mid a, b) = \frac{e^{b x} e^{-e^{x}/a}}{a^{b} \Gamma(b)}, \quad -\infty < x < \infty, a > 0, b > 0.


Ref: https://www.math.wm.edu/~leemis/chart/UDR/PDFs/Loggamma.pdf

ClosedFormExpectations.xlog2xMethod

Return x * log(x)^2 for x ≥ 0, handling $x = 0$ by taking the downward limit.

julia> xlog2x(0)
0.0
Statistics.meanMethod
mean(::ClosedFormExpectation, f, q)

Compute the E_q[f(x)] where q is a distribution and f is a function.

Statistics.meanMethod
mean(::ClosedWilliamsProduct, f, q)

Suppose q is a distribution with density parameterized by θ and f is a function.

Compute the Eq[f(x) ∇θ log q(x; θ)] where q is a distribution and f is a function.