| marginalLikelihood.CatDirichlet | R Documentation |
Generate the marginal likelihood of the following model 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 model structure and prior parameters are stored in a "CatDirichlet" object.
Marginal likelihood is the likelihood of x|alpha.
## S3 method for class 'CatDirichlet' marginalLikelihood(obj, x, LOG = TRUE, ...)
obj |
A "CatDirichlet" object. |
x |
numeric/integer/character vector, observed Categorical samples. |
LOG |
Return the log density if set to "TRUE". |
... |
Additional arguments to be passed to other inherited types. |
numeric, the marginal likelihood.
Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
CatDirichlet, marginalLikelihood_bySufficientStatistics.CatDirichlet
obj <- CatDirichlet(gamma=list(alpha=runif(26,1,2),uniqueLabels = letters)) x <- sample(letters,size = 20,replace = TRUE) marginalLikelihood(obj=obj,x=x,LOG = TRUE) #marginal likelihood ss <- sufficientStatistics(obj = obj,x=x) marginalLikelihood_bySufficientStatistics(obj=obj,ss = ss,LOG = TRUE)