logLikelihoodLGLFM {aibd} | R Documentation |
The log of the likelihood of a feature allocation from the linear Gaussian latent
feature model (LGLFM) is computed. The standard deviation of the error term
(sdX
) may be supplied or the associated precision (precisionX
)
can be provided instead. Likewise, only one of sdA
and
precisionA
should be supplied.
logLikelihoodLGLFM( featureAllocation, X, precisionX, precisionA, sdX, sdA, implementation = "scala" )
featureAllocation |
An N-by-K binary feature allocation matrix. |
X |
An N-by-D matrix of observed data. |
precisionX |
The scalar precision of the data error variance. This must
be specified if |
precisionA |
The scalar precision of a latent feature. This must be
specified if |
sdX |
The scalar standard deviation of the data error variance. This
must be specified if |
sdA |
The scalar precision of a latent feature. This must be specified
if |
implementation |
The default of |
A numeric vector giving the log of the likelihood.
This function is an implementation of the log of Equation (26) in "The Indian Buffet Process: An Introduction and Review" by Griffiths and Ghahramani (2011) in the Journal of Machine Learning.
# Regardless of size, the initial warmup can exceed CRAN's 5 seconds threshold sigx <- 0.1 siga <- 1.0 dimA <- 1 nItems <- 8 # Should be a multiple of 4 Z <- matrix(c(1,0,1,1,0,1,0,0),byrow=TRUE,nrow=nItems,ncol=2) A <- matrix(rnorm(ncol(Z)*dimA,sd=siga),nrow=ncol(Z),ncol=dimA) e <- rnorm(nrow(Z)*ncol(A),0,sd=sigx) X <- Z %*% A + e logLikelihoodLGLFM(Z, X, sdX=sigx, sdA=siga)