marginalLikelihood_bySufficientStatistics.LinearGaussianGaussian: Marginal likelihood of a "LinearGaussianGaussian" object,...

In bbricks: Bayesian Methods and Graphical Model Structures for Statistical Modeling

Description

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)

Usage

## S3 method for class 'LinearGaussianGaussian'
marginalLikelihood_bySufficientStatistics(obj, ss, LOG = TRUE, ...)

Arguments

obj	A "LinearGaussianGaussian" object.
ss	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().
LOG	Return the log density if set to "TRUE".
...	Additional arguments to be passed to other inherited types.

Value

numeric, the marginal likelihood.

See Also

LinearGaussianGaussian, marginalLikelihood.LinearGaussianGaussian

bbricks documentation built on July 8, 2020, 7:29 p.m.