Generate the marginal likelihood of the following model structure:
$$x \sim Gaussian(A z + b, Sigma)$$
$$z \sim Gaussian(m,S)$$
Where Sigma is known. A is a \(dimx x dimz\) matrix, x is a \(dimx x 1\) random vector, z is a \(dimz x 1\) random vector, b is a \(dimm x 1\) vector. Gaussian() is the Gaussian distribution. See ?dGaussian for the definition of Gaussian distribution.
The model structure and prior parameters are stored in a "LinearGaussianGaussian" object.
Marginal likelihood = p(x|m,S,Sigma)
# S3 method for LinearGaussianGaussian
marginalLikelihood_bySufficientStatistics(obj, ss, LOG = TRUE, ...)A "LinearGaussianGaussian" object.
Sufficient statistics of x. In Gaussian-Gaussian case the sufficient statistic of sample x is a object of type "ssGaussianMean", it can be generated by the function sufficientStatistics().
Return the log density if set to "TRUE".
Additional arguments to be passed to other inherited types.
numeric, the marginal likelihood.
LinearGaussianGaussian, marginalLikelihood.LinearGaussianGaussian