Old and New Matrix Algebra Useful for Statistics

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Sparse Gaussian Process Classification With Multiple Classes (2004, methodology): “In order to derive the gradient, we make use of matrix calculus rules assembled in a formidable paper by Minka [9] (who draws on the book by Magnus and Neudecker [8]).”
Complex-Valued Matrix Differentiation: Techniques and Key Results (2007, background): “Some of the basic differential results in Table II can be derived by means of (4), and others can be derived by generalizing some of the results found in [7], [9] to the complex differential case.”
Collected Matrix Derivative Results for Forward and Reverse Mode Algorithmic Differentiation (2008, methodology): “This is precisely the approach used by Minka [ 13 ] (based on Magnus and Neudecker [11]) even though his results are not expressed in AD notation, and the reverse mode sensitivities appear to be an end in themselves, rather than a building block within an algorithmic differentiation of a much larger”