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?

General Usage

gdalle May 4, 2023, 7:19pm 3

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

Topic		Replies	Views
The fastest way to calculate matrix inversion Performance package , performance , linearalgebra , precision	9	8257	June 15, 2021
Most efficient way of computing B'* inv(A)* B for A large Specific Domains linearalgebra	30	981	September 30, 2020
Explicit Inversion of a random matrix performs the same as Symmetric & SPD Performance question , linearalgebra	6	1093	October 25, 2018
What is the most efficient way to compute the inverse of a sparse matrix? Performance question , linearalgebra , sparse	15	5091	May 5, 2023
Inverting a symmetric matrix is not faster than inverting a random one Performance linearalgebra	16	1340	March 28, 2023

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

Related topics