Generate multiple sequences of correlated random variables

In the previous post, a method for generating two sequences of correlated random variables was discussed. Generation of multiple sequences of correlated random variables, given a correlation matrix is discussed here. Correlation Matrix Correlation matrix defines correlation among N variables. It is a symmetric matrix with the element equal to the correlation coefficient between the … Read more

Cholesky decomposition: Python & Matlab

Cholesky decomposition is an efficient method for inversion of symmetric positive-definite matrices. Let’s demonstrate the method in Python and Matlab. Cholesky factor Any symmetric positive definite matrix can be factored as where is lower triangular matrix. The lower triangular matrix is often called “Cholesky Factor of ”. The matrix can be interpreted as square root … Read more

Check Positive Definite Matrix in Matlab

It is often required to check if a given matrix is positive definite or not. Three methods to check the positive definiteness of a matrix were discussed in a previous article . I will utilize the test method 2 to implement a small matlab code to check if a matrix is positive definite.The test method … Read more

Solve Triangular Matrix – Forward & Backward Substitution

Key focus: Know the expressions to solve triangular matrix using forward and backward substituting techniques and the FLOPS required for solving it. Forward Substitution: Consider a set of equations in a matrix form , where A is a lower triangular matrix with non-zero diagonal elements. The equation is re-written in full matrix form as It … Read more

Why Cholesky Decomposition ? A sample case:

Matrix inversion is seen ubiquitously in signal processing applications. For example, matrix inversion is an important step in channel estimation and equalization. For instance, in GSM normal burst, 26 bits of training sequence are put in place with 114 bits of information bits. When the burst travels over the air interface (channel), it is subject … Read more

Matrix Algebra for Signal Processing

Key focus : Essential matrix algebra: formation of matrices, determinants, rank, inverse & transpose of matrix and solving simultaneous equations. I thought of making a post on Cholesky Decomposition, which is a very essential technique in digital signal processing applications like generating correlated random variables, solving linear equations, channel estimation etc.., But jumping straight to … Read more