marginalLikelihood_bySufficientStatistics.GaussianInvWishart

Sufficient statistics of x. In Gaussian and Inverse-Wishart case the sufficient statistic of sample x is a object of type "ssGaussianVar", 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.

Generate the marginal likelihood of the following model structure:
 $$x \sim Gaussian(mu,Sigma)$$
 $$Sigma \sim InvWishart(v,S)$$
mu is known. Gaussian() is the Gaussian distribution. See <code>?dGaussian</code> and <code>?dInvWishart</code> for the definition of the distributions.
The model structure and prior parameters are stored in a "GaussianInvWishart" object. 
Marginal likelihood = p(x|v,S,mu)

A set of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include linear Gaussian systems, Gaussian and Normal-Inverse-Wishart conjugate structure, Gaussian and Normal-Inverse-Gamma conjugate structure, Categorical and Dirichlet conjugate structure, Dirichlet Process on positive integers, Dirichlet Process in general, Hierarchical Dirichlet Process ... Tasks include updating posteriors, sampling from posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See <https://chenhaotian.github.io/Bayesian-Bricks/> to get started.

marginalLikelihood_bySufficientStatistics.GaussianInvWishart: Marginal likelihood of a "GaussianInvWishart" object, using sufficient statistics

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See Also