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In mathematics, a stochastic matrix (also termed probability matrixtransition matrix,[1] substitution matrix, or Markov matrix) is a matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability. It has found use in probability theorystatistics and linear algebra, as well ascomputer science and population genetics. There are several different definitions and types of stochastic matrices:

right stochastic matrix is a square matrix of nonnegative real numbers, with each row summing to 1.
left stochastic matrix is a square matrix of nonnegative real numbers, with each column summing to 1.
doubly stochastic matrix is a square matrix of nonnegative real numbers with each row and column summing to 1.

In the same vein, one may define stochastic vector (also called probability vector) as a vector whose elements are nonnegative real numbers which sum to 1. Thus, each row of a right stochastic matrix (or column of a left stochastic matrix) is a stochastic vector.

A common convention in English language mathematics literature is to use row vectors of probabilities and right stochastic matrices rather than column vectors of probabilities and left stochastic matrices.

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