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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, the matrix analytic method is a technique to compute the stationary probability distribution of a Markov chain which has a repeating structure (after some point) and a state space which grows unboundedly in no more than one dimension.
D. G. Champernowne built a Markov chain model of the distribution of income in 1953. [86] Herbert A. Simon and co-author Charles Bonini used a Markov chain model to derive a stationary Yule distribution of firm sizes. [87] Louis Bachelier was the first to observe that stock prices followed a random walk. [88]
Consider a finite state irreducible aperiodic Markov chain with state space and (unique) stationary distribution (is a probability vector). Suppose that we come up with a probability distribution on the set of maps : with the property that for every fixed , its image () is distributed according to the transition probability of from state .
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 ...
Markov chains with generator matrices or block matrices of this form are called M/G/1 type Markov chains, [13] a term coined by Marcel F. Neuts. [ 14 ] [ 15 ] An M/G/1 queue has a stationary distribution if and only if the traffic intensity ρ = λ E ( G ) {\displaystyle \rho =\lambda \mathbb {E} (G)} is less than 1, in which case the unique ...
A finite-state machine can be used as a representation of a Markov chain. Assuming a sequence of independent and identically distributed input signals (for example, symbols from a binary alphabet chosen by coin tosses), if the machine is in state y at time n , then the probability that it moves to state x at time n + 1 depends only on the ...
A Markov process is called a reversible Markov process or reversible Markov chain if there exists a positive stationary distribution π that satisfies the detailed balance equations [13] =, where P ij is the Markov transition probability from state i to state j, i.e. P ij = P(X t = j | X t − 1 = i), and π i and π j are the equilibrium probabilities of being in states i and j, respectively ...