Distribution Functions

Common Functions

log posterior predictive

The log posterior predictive can be computed using:

p = logpostpred(d::ConjugatePostDistribution, x)

resulting in a scalar if x and d is univariate or if x and d is multivariate. Otherwise, a vector is returned.

adding observations

Observations can be added to the conjugate posterior distribution using:

add!(d::ConjugatePostDistribution, x)

for inplace operations or using

d = add(d::ConjugatePostDistribution, x)

resulting in a new distribution d.

removing observations

Observation can be removed from a conjugate posterior distribution using:

remove!(d::ConjugatePostDistribution, x)

for inplace operations of using

d = remove(d::ConjugatePostDistribution, x)

resulting in a new distribution d.

posterior parameters

The posterior parameters of a distribution can be obtained using:

parameters = posteriorParameters(d::ConjugatePostDistribution)

those parameters are returned as tuples, e.g. parameters = (μ, σ) in the case of NormalNormal distributions.