| marginalLikelihood_bySufficientStatistics.CatDirichlet | R Documentation |
Generate the marginal likelihood of a set of observations 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_bySufficientStatistics(obj, ss, LOG = TRUE, ...)
obj |
A "CatDirichlet" object. |
ss |
Sufficient statistics of x. In Categorical-Dirichlet case the sufficient statistic of sample x can be either x itself, of an "ssCat" object generated by the function sufficientStatistics.CatDirichlet(). |
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.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)