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  2. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    Including the fact that the sum of each the rows in P is 1, ... Master equation; Quantum Markov chain; Semi-Markov process; Stochastic cellular automaton;

  3. Continuous-time Markov chain - Wikipedia

    en.wikipedia.org/wiki/Continuous-time_Markov_chain

    A continuous-time Markov chain (CTMC) is a continuous stochastic process in which, for each state, the process will change state according to an exponential random variable and then move to a different state as specified by the probabilities of a stochastic matrix.

  4. Stochastic matrix - Wikipedia

    en.wikipedia.org/wiki/Stochastic_matrix

    A substochastic matrix is a real square matrix whose row sums are all ; In the same vein, one may define a probability vector as a vector whose elements are nonnegative real numbers which sum to 1. Thus, each row of a right stochastic matrix (or column of a left stochastic matrix) is a probability vector.

  5. Balance equation - Wikipedia

    en.wikipedia.org/wiki/Balance_equation

    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]

  6. Transition-rate matrix - Wikipedia

    en.wikipedia.org/wiki/Transition-rate_matrix

    and therefore the rows of the matrix sum to zero. Up to a global sign, a large class of examples of such matrices is provided by the Laplacian of a directed, weighted graph . The vertices of the graph correspond to the Markov chain's states.

  7. 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.

  8. Kolmogorov equations - Wikipedia

    en.wikipedia.org/wiki/Kolmogorov_equations

    In the context of a continuous-time Markov process with jumps, see Kolmogorov equations (Markov jump process). In particular, in natural sciences the forward equation is also known as master equation. In the context of a diffusion process, for the backward Kolmogorov equations see Kolmogorov backward equations (diffusion).

  9. Absorbing Markov chain - Wikipedia

    en.wikipedia.org/wiki/Absorbing_Markov_chain

    A basic property about an absorbing Markov chain is the expected number of visits to a transient state j starting from a transient state i (before being absorbed). This can be established to be given by the (i, j) entry of so-called fundamental matrix N, obtained by summing Q k for all k (from 0 to ∞).