7.8. Multivariate Gaussian Distribution#

Definition 7.32

A random vector \(X = [X_1, \dots, X_n]^T\) is called Gaussian random vector if

\[ \langle t , X \rangle = X^T t = \sum_{i = 1}^n t_i X_i = t_1 X_1 + \dots + t_n X_n \]

follows a normal distribution for all \(t = [t_1, \dots, t_n ]^T \in \RR^n\). The components \(X_1, \dots, X_n\) are called jointly Gaussian. It is denoted by \(X \sim \NNN_n (\mu, \Sigma)\) where \(\mu\) is its mean vector and \(\Sigma\) is its covariance matrix.

Let \(X \sim \NNN_n (\mu, \Sigma)\) be a Gaussian random vector. The subscript \(n\) denotes that it takes values over the space \(\RR^n\). We assume that \(\Sigma\) is invertible. Its PDF is given by

\[ f_X (x) = \frac{1}{(2\pi)^{n / 2} \det (\Sigma)^{1/2} } \exp \left \{- \frac{1}{2} (x - \mu)^T \Sigma^{-1} (x - \mu) \right\}. \]


\[ \EE [X] = \mu \in \RR^n. \]
\[ \EE[XX^T] = \Sigma + \mu \mu^T. \]
\[ \Cov[X] = \EE[XX^T] - \EE[X]\EE[X]^T = \Sigma. \]

Let \(Y = A X + b\) where \(A \in \RR^{n \times n}\) is an invertible matrix and \(b \in \RR^n\). Then

\[ Y \sim \NNN_n (A \mu + b , A \Sigma A^T). \]

\(Y\) is also a Gaussian random vector with the mean vector being \(A \mu + b\) and the covariance matrix being \(A \Sigma A^T\). This essentially is a change in basis in \(\RR^n\).

The CF is given by

\[ \Psi_X(j \omega) \exp \left ( j \omega^T x - \frac{1}{2} \omega^T \Sigma \omega \right ), \quad \omega \in \RR^n. \]

7.8.1. Whitening#

Usually we are interested in making the components of \(X\) uncorrelated. This process is known as whitening. We are looking for a linear transformation \(Y = A X + b\) such that the components of \(Y\) are uncorrelated. i.e. we start with

\[ X \sim \NNN_n (\mu, \Sigma) \]

and transform \(Y = A X + b\) such that

\[ Y \sim \NNN_n (0, I_n) \]

where \(I_n\) is the \(n\)-dimensional identity matrix. Whitening by Eigen Value Decomposition#


\[ \Sigma = E \Lambda E^T \]

be the eigen value decomposition of \(\Sigma\) with \(\Lambda\) being a diagonal matrix and \(E\) being an orthonormal basis.


\[ \Lambda^{\frac{1}{2}} = \Diag (\lambda_1^{\frac{1}{2}}, \dots, \lambda_n^{\frac{1}{2}}). \]

Choose \(B = E \Lambda^{\frac{1}{2}}\) and \(A = B^{-1} = \Lambda^{-\frac{1}{2}} E^T\).

\[ \Cov (B^{-1} X) = \Cov (A X) = \Lambda^{-\frac{1}{2}} E^T \Sigma E \Lambda^{-\frac{1}{2}} = I. \]
\[ \EE [B^{-1} X] = B^{-1} \mu \iff \EE [B^{-1} (X - \mu)] = 0. \]

Thus the random vector \(Y = [B^{-1} (X - \mu)\) is a whitened vector of uncorrelated components. Causal Whitening#

We want that the transformation be causal, i.e. \(A\) should be a lower triangular matrix. We start with

\[ \Sigma = L D L^T = (L D^{\frac{1}{2}} ) (D^{\frac{1}{2}} L^T). \]

Choose \(B = L D^{\frac{1}{2}} \) and \(A = B^{-1} = D^{-\frac{1}{2}} L^{-1}\). Clearly, \(A\) is lower triangular.

The transformation is \(Y = [B^{-1} (X - \mu)\).