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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.
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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.
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.
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.
Modifying the Sa-parameter, that captures the thermodynamic state of the polymer matrix, allows us to correctly predict the macroscopic response after yield.
Small quantities of graphene fillers in the polymeric matrix allow us to obtain a novel class of lightweight nanocomposites with outstanding electrical properties.
The gold matrix allows us to release from metal oxidation and, associated with PTFE (chemically inert), to isolate easily the composite coating by dissolution of the metal support.
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