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The following matrices find their main application in statistics and probability theory. Bernoulli matrix — a square matrix with entries +1, −1, with equal probability of each. Centering matrix — a matrix which, when multiplied with a vector, has the same effect as subtracting the mean of the components of the vector from every component. Correlation matrix — a symmetric n×n matrix, formed by the pairwise correlation coefficients of several random variables. Covariance matrix — a symmetric n×n matrix, formed by the pairwise covariances of several random variables. Sometimes called a dispersion matrix. Dispersion matrix — another name for a covariance matrix. Doubly stochastic matrix — a non-negative matrix such that each row and each column sums to 1 (thus the matrix is both left stochastic and right stochastic) Fisher information matrix — a matrix representing the variance of the partial derivative, with respect to a parameter, of the log of the likelihood function of a random variable. Hat matrix - a square matrix used in statistics to relate fitted values to observed values. Precision matrix — a symmetric n×n matrix, formed by inverting the covariance matrix. Also called the information matrix. Stochastic matrix — a non-negative matrix describing a stochastic process. The sum of entries of any row is one. Transition matrix — a matrix representing the probabilities of conditions changing from one state to another in a Markov chain |
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