Create an object of type "LinearGaussianGaussian", which represents the Linear Gaussian and Gaussian conjugate structure:
x \sim Gaussian(A z + b, Sigma)
z \sim Gaussian(m,S)
Where Sigma is known. A is a m x n matrix, x is a m x 1 random vector, z is a n x 1 random vector, b is a n x 1 vector. Gaussian() is the Gaussian distribution. See
?dGaussian for the definition of Gaussian distribution.
The created object will be used as a place for recording and accumulating information in the related inference/sampling functions such as posterior(), posteriorDiscard(), MAP(), marginalLikelihood(), dPosteriorPredictive(), rPosteriorPredictive() and so on.
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an object of type "LinearGaussianGaussian". If "objCopy" is not NULL, the function create a new "LinearGaussianGaussian" object by copying the content from objCopy, otherwise this new object will be created by using "ENV" and "gamma". Default NULL.
environment, specify where the object will be created.
list, a named list of parameters, gamma=list(Sigma,m,S). Where gamma$Sigma is the known covariance matrix of x, gamma$m and gamma$S are the prior mean and covariance matrix of z.
An object of class "LinearGaussianGaussian".
Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
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