In this case, μ(Θ t ) can be quantified as the maximum magnitude off-diagonal element of ({mathbf {D}}_{t}={breve {boldsymbol {Theta }}}_{t}^{T}{breve {{boldsymbol {Theta }}}_{t}}), where ({breve {{boldsymbol {Theta }}}_{t}}) is obtained by normalizing the columns of Θ t.
By normalizing the columns of a counts matrix A E ba, we can interpret each entry as the probability that a→ b in environment E. When a vector of amino acid counts is multiplied by this matrix, it changes according to the mutation probabilities encoded in the matrix.
The scaling ambiguity can be eliminated by normalizing the first column of Hsr or the first row of Hrd to one.
The Markov transition matrix P can be obtained by normalizing the adjacency matrix to have each column sum up to 1, e.g. begin{aligned} P_{ij}=frac{A_{ij}}{sum _{k=1}^{n}A_{kj}} end{aligned} (1)or begin{aligned} P=AD^{-1}.
The score S of each column was evaluated by normalizing the number n by the number of all possible codon combinations N S == n/N.
The %chSp was calculated by normalizing the number of residues in the ortholog sequence that occur only once in individual alignment columns [ 4].
where is a diagonal matrix that normalizes the columns of to unit norm.
Normalize the columns of X. Step 2.
Step 2: Normalize the columns of A and y to have unit l 2 -norm.
The mutation frequency matrix P can be computed from C by normalizing each column of C to sum to one.
