Implement estimators of large-scale sparse Gaussian densities
Computing log-likelihood for Gaussian distribution requires computation of log-determinant of the covariance (or precision) matrix. Usual approach, based on Cholesky factorization, often suffers from huge memory requirement for fill-in phenomenon when the covariance matrix is huge and sparse. This project aims for computing the log-determinant in an efficient way, which makes use of a bunch of techniques from numerical linear algebra and complex analysis. The objective of this project is to approximate the matrix-logarithm up to an arbitrary precision and evaluate log-determinant with reduced memory requirement, targeting for speeding up by enabling parallel computation of the components involved.
|File name||Size||Date submitted|
|Soumyajit_De.tar.gz||303.8 KB||September 28 2013 19:49 UTC|