This definition of the factor matrices allows us to rewrite (3) according to the notation proposed in [7] (4).
Comparing two matrices allows us to get a preliminary intuition of the extent to which co-editing patterns are non-random.
Saltation matrices allow us to handle any jumps in the system (or its linearisation) when it changes from one dynamical regime to another.
Then the set of autocorrelation matrices allows us to formulate a series of linear equations relating the outer products of the scaled steering vectors.
The conditional probability distributions given by the w matrices allow us to model read position and base-dependent substitution processes.
Applying a RADICAL approach to different matrices allows us to examine the phylogenetic contribution of each codon position, the effect of saturated sites on phylogenetic analysis, and the amount of agreement/disagreement for nodes among data matrices.
We closely approximate the Bayesian posterior distribution over connectivity matrices, allowing us to compute established design criteria such as the information gain, which cannot be done using maximum a posteriori (MAP) estimation.
Examining the covariation matrix allowed us to minimize the degree of multicolinearity in our dataset by removing variables that displayed r2 values greater than 0.85.
It's times like these when a kink in the matrix allows us--depending on how close we are to the epicenter of the madness--to take stock of our own situations and motives.
Our approach, using adjacency matrix, allow us to identify exact motifs, hence, no p-values are associated with the findings.
