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  2. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    If the Markov chain is time-homogeneous, then the transition matrix P is the same after each step, so the k-step transition probability can be computed as the k-th power of the transition matrix, P k. If the Markov chain is irreducible and aperiodic, then there is a unique stationary distribution π. [41]

  3. Kolmogorov's criterion - Wikipedia

    en.wikipedia.org/wiki/Kolmogorov's_criterion

    Consider this figure depicting a section of a Markov chain with states i, j, k and l and the corresponding transition probabilities. Here Kolmogorov's criterion implies that the product of probabilities when traversing through any closed loop must be equal, so the product around the loop i to j to l to k returning to i must be equal to the loop the other way round,

  4. Markov chain mixing time - Wikipedia

    en.wikipedia.org/wiki/Markov_chain_mixing_time

    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.

  5. Discrete-time Markov chain - Wikipedia

    en.wikipedia.org/wiki/Discrete-time_Markov_chain

    A Markov chain with two states, A and E. In probability, a discrete-time Markov chain (DTMC) is a sequence of random variables, known as a stochastic process, in which the value of the next variable depends only on the value of the current variable, and not any variables in the past.

  6. Coupling from the past - Wikipedia

    en.wikipedia.org/wiki/Coupling_from_the_past

    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 .

  7. Autocorrelation - Wikipedia

    en.wikipedia.org/wiki/Autocorrelation

    In statistics, the autocorrelation of a real or complex random process is the Pearson correlation between values of the process at different times, as a function of the two times or of the time lag.

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    mail.aol.com

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  9. Aperiodic graph - Wikipedia

    en.wikipedia.org/wiki/Aperiodic_graph

    An aperiodic graph. The cycles in this graph have lengths 5 and 6; therefore, there is no k > 1 that divides all cycle lengths. A strongly connected graph with period three. In the mathematical area of graph theory, a directed graph is said to be aperiodic if there is no integer k > 1 that divides the length of every cycle of the graph.