Suppose that I have \mathbf{y}\sim N(\boldsymbol{\mu}, \mathbf{A} + \mathbf{UCU'}), where \mathbf{A} and \mathbf{C} are diagonal matrices. Fortunately, the covariance term follows the pattern of a sparse symmetric Woodbury matrix. I coded up the following function to evaluate the log-density of a …

If they aren’t already, it may help if A and C are of the Diagonal type. Their inverses will take less memory and operations with them will be faster (no need to check for diagonality for inv and logdet, no need to add off-diagonals for +). While inv and logdet on diagonal matrices are very cheap, …

Is there a more efficient way to compute the log-density of a Multivariate normal with a Symmetric Woodbury Covariance?

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gdalle May 4, 2023, 7:19pm 3

Can you profile the code to see which lines take longer on a typical example?

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Is there a more efficient way to compute the log-density of a Multivariate normal with a Symmetric Woodbury Covariance?

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