Introduction to CatSIM | How CatSIM works | Constructing a binary illustration | Constructing a multiclass example | Computing Image Similarity | CatSIM options | Advanced Functionality | References

Details of the modeling choices | Classification Rule | Matrix Variate Normal Populations | Estimated Minimum ECM Rule for Two Matrix Variate Normal Populations | How to classify based on this: | If there are equal covariances: | Generalizing to multiple classes | Structure of the objects | Session info | All the code for easy copying

Matrix Variate Distributions | Matrix Variate Normal Distributions | Parameter estimation for matrix variate normal distributions | Session Information | All the code for easy copying | References

Estimation of the Matrix Variate t Distribution | E-step | CM Steps | Applications and results | With $\nu$ known | With $\nu$ unknown | Use for classification | Session info | All the code for easy copying | References

Matrix Variate Mixture Modeling with the $t$ Distribution | Usage | matrixmixture function | Initialization function | Session Information | All the code for easy copying | References

How the Wishart distribution works | The Inverse Wishart distribution | The Pseudo-Wishart and Generalized Inverse Wishart | Density computation | Other functions | References