The stationary distribution is the row vector .
Conditions: The transition matrix is .; is a row vector satisfying and .
The stationary distribution is the row vector .
Conditions: The transition matrix is .; is a row vector satisfying and .
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The stationary distribution is the row vector .
Conditions: The transition matrix is .; is a row vector satisfying and .
The stationary distribution is the row vector .
Conditions: The transition matrix is .; is a row vector satisfying and .
The equations derived from are linearly dependent because the rows of the transition matrix sum to 1. This implies that the sum of the equations is identically zero, leaving only independent constraints for states.
Conditions: is a stochastic matrix (rows sum to 1).; is a row vector of probabilities.
The equations derived from are linearly dependent because the rows of the transition matrix sum to 1. This implies that the sum of the equations is identically zero, leaving only independent constraints for states.
Conditions: is a stochastic matrix (rows sum to 1).; is a row vector of probabilities.
To find a stationary distribution for a Markov chain, one solves the system of linear equations given by , subject to the constraint that the probabilities in sum to 1.
Conditions: is the transition matrix of the Markov chain.; is a row vector of probabilities.
To find a stationary distribution for a Markov chain, one solves the system of linear equations given by , subject to the constraint that the probabilities in sum to 1.
Conditions: is the transition matrix of the Markov chain.; is a row vector of probabilities.
We add the equation because is a valid probability distribution, meaning its elements must sum to 1. This provides the second independent equation needed to solve the underdetermined system created by the redundant balance equations.
Conditions: is a probability distribution over the states.; The state space has exactly two states, a and b.
We add the equation because is a valid probability distribution, meaning its elements must sum to 1. This provides the second independent equation needed to solve the underdetermined system created by the redundant balance equations.
Conditions: is a probability distribution over the states.; The state space has exactly two states, a and b.
By substituting into , we get . Combining the terms yields , and solving for gives .
Conditions: The balance equation gives , which rearranges to .; The normalization equation is .
By substituting into , we get . Combining the terms yields , and solving for gives .
Conditions: The balance equation gives , which rearranges to .; The normalization equation is .