Description Usage Arguments Value References See Also Examples
View source: R/Categorical_Inference.r
Create an object of type "CatDirichlet", which represents the Categorical (Multinomial) and Dirichlet conjugate structure:
pi|alpha \sim Dir(alpha)
x|pi \sim Categorical(pi)
Where Dir() is the Dirichlet distribution, Categorical() is the Categorical distribution. See ?dDir
and dCategorical
for the definitions of these distribution.
The created object will be used as a place for recording and accumulating information in the related inference/sampling functions such as posterior(), posteriorDiscard(), MAP(), marginalLikelihood(), dPosteriorPredictive(), rPosteriorPredictive() and so on. A categorical distribution is defined on a set of unique labels, usually these labels are integers, they can also be characters and factors.
1 2 3 4 5 | CatDirichlet(
objCopy = NULL,
ENV = parent.frame(),
gamma = list(alpha = 1, uniqueLabels = 1L)
)
|
objCopy |
an object of type "CatDirichlet". If "objCopy" is not NULL, the function create a new "CatDirichlet" object by copying the content from objCopy, otherwise this new object will be created by using "ENV" and "gamma". Default NULL. |
ENV |
environment, specify where the object will be created. |
gamma |
list, a named list of parameters, gamma=list(alpha,uniqueLabels). Where gamma$alpha is a numeric vector specifying the parameters of the Dirichlet distribution, gamma$uniqueLabels is a integer/character vector specifying the unique category labels of the Categorical distribution. |
An object of class "CatDirichlet".
Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
posterior.CatDirichlet
,posteriorDiscard.CatDirichlet
,MAP.CatDirichlet
,MPE.CatDirichlet
,marginalLikelihood.CatDirichlet
,rPosteriorPredictive.CatDirichlet
,dPosteriorPredictive.CatDirichlet
...
1 2 | obj <- CatDirichlet(gamma=list(alpha=c(1,2,1),uniqueLabels = letters[1:3]))
obj #print the content
|
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