Search results
Results from the WOW.Com Content Network
In probability theory and statistics, a cross-covariance matrix is a matrix whose element in the i, j position is the covariance between the i-th element of a random vector and j-th element of another random vector. When the two random vectors are the same, the cross-covariance matrix is referred to as covariance matrix.
The cross-correlation matrix of two random vectors is a matrix containing as elements the cross-correlations of all pairs of elements of the random vectors. The cross-correlation matrix is used in various digital signal processing algorithms.
A tetrad is the association of a pair of homologous chromosomes (4 sister chromatids) physically held together by at least one DNA crossover. This physical attachment allows for alignment and segregation of the homologous chromosomes in the first meiotic division. In most organisms, each replicated chromosome (composed of two identical sisters ...
To this end, we will construct a 20x20 matrix where the (,) th entry is equal to the probability of the th amino acid being transformed into the th amino acid in a certain amount of evolutionary time. There are many different ways to construct such a matrix, called a substitution matrix. Here are the most commonly used ones:
In genetics, a chiasma (pl.: chiasmata) is the point of contact, the physical link, between two (non-sister) chromatids belonging to homologous chromosomes. At a given chiasma, an exchange of genetic material can occur between both chromatids, what is called a chromosomal crossover, but this is much more frequent during meiosis than mitosis. [1]
A pair of homologous chromosomes, or homologs, is a set of one maternal and one paternal chromosome that pair up with each other inside a cell during fertilization. Homologs have the same genes in the same loci , where they provide points along each chromosome that enable a pair of chromosomes to align correctly with each other before ...
To construct a self-similarity matrix, one first transforms a data series into an ordered sequence of feature vectors = (,, …,), where each vector describes the relevant features of a data series in a given local interval. Then the self-similarity matrix is formed by computing the similarity of pairs of feature vectors
In the definition of similarity, if the matrix P can be chosen to be a permutation matrix then A and B are permutation-similar; if P can be chosen to be a unitary matrix then A and B are unitarily equivalent. The spectral theorem says that every normal matrix is unitarily equivalent to some diagonal matrix.