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In mathematics, a stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability . [ 1 ] [ 2 ] : 10 It is also called a probability matrix , transition matrix , substitution matrix , or Markov matrix .
The word stochastic is used to describe other terms and objects in mathematics. Examples include a stochastic matrix, which describes a stochastic process known as a Markov process, and stochastic calculus, which involves differential equations and integrals based on stochastic processes such as the Wiener process, also called the Brownian ...
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.
Explain: The original matrix equation is equivalent to a system of n×n linear equations in n×n variables. And there are n more linear equations from the fact that Q is a right stochastic matrix whose each row sums to 1. So it needs any n×n independent linear equations of the (n×n+n) equations to solve for the n×n variables.
The class of doubly stochastic matrices is a convex polytope known as the Birkhoff polytope.Using the matrix entries as Cartesian coordinates, it lies in an ()-dimensional affine subspace of -dimensional Euclidean space defined by independent linear constraints specifying that the row and column sums all equal 1.
In mathematics, an orthostochastic matrix is a doubly stochastic matrix whose entries are the squares of the absolute values of the entries of some orthogonal matrix.
In 1926, Van der Waerden conjectured that the minimum permanent among all n × n doubly stochastic matrices is n!/n n, achieved by the matrix for which all entries are equal to 1/n. [18] Proofs of this conjecture were published in 1980 by B. Gyires [ 19 ] and in 1981 by G. P. Egorychev [ 20 ] and D. I. Falikman; [ 21 ] Egorychev's proof is an ...
If A is an n × n matrix with strictly positive elements, then there exist diagonal matrices D 1 and D 2 with strictly positive diagonal elements such that D 1 AD 2 is doubly stochastic. The matrices D 1 and D 2 are unique modulo multiplying the first matrix by a positive number and dividing the second one by the same number. [1] [2]