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If a Markov chain has a stationary distribution, then it can be converted to a measure-preserving dynamical system: Let the probability space be =, where is the set of all states for the Markov chain. Let the sigma-algebra on the probability space be generated by the cylinder sets.
Stationary distribution may refer to: . Discrete-time Markov chain § Stationary distributions and continuous-time Markov chain § Stationary distribution, a special distribution for a Markov chain such that if the chain starts with its stationary distribution, the marginal distribution of all states at any time will always be the stationary distribution.
Markov chose 20,000 letters from Pushkin’s Eugene Onegin, classified them into vowels and consonants, and counted the transition probabilities. The stationary distribution is 43.2 percent vowels and 56.8 percent consonants, which is close to the actual count in the book.
We say is Markov with initial distribution and rate matrix to mean: the trajectories of are almost surely right continuous, let be a modification of to have (everywhere) right-continuous trajectories, (()) = + almost surely (note to experts: this condition says is non-explosive), the state sequence (()) is a discrete-time Markov chain with ...
For a continuous time Markov chain (CTMC) with transition rate matrix, if can be found such that for every pair of states and = holds, then by summing over , the global balance equations are satisfied and is the stationary distribution of the process. [5]
In probability theory, Kelly's lemma states that for a stationary continuous-time Markov chain, a process defined as the time-reversed process has the same stationary distribution as the forward-time process. [1] The theorem is named after Frank Kelly. [2] [3] [4] [5]
In probability theory, the mixing time of a Markov chain is the time until the Markov chain is "close" to its steady state distribution.. More precisely, a fundamental result about Markov chains is that a finite state irreducible aperiodic chain has a unique stationary distribution π and, regardless of the initial state, the time-t distribution of the chain converges to π as t tends to infinity.
In the mathematical theory of Markov chains, the Markov chain tree theorem is an expression for the stationary distribution of a Markov chain with finitely many states. It sums up terms for the rooted spanning trees of the Markov chain, with a positive combination for each tree.