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  2. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    4.3.2 Time-homogeneous Markov chain with a ... the elements q ij are non-negative and describe the rate ... and the fact that Q is a stochastic matrix to solve for ...

  3. Markov decision process - Wikipedia

    en.wikipedia.org/wiki/Markov_decision_process

    These elements encompass the understanding of cause and effect, the management of uncertainty and nondeterminism, and the pursuit of explicit goals. [4] The name comes from its connection to Markov chains, a concept developed by the Russian mathematician Andrey Markov.

  4. Examples of Markov chains - Wikipedia

    en.wikipedia.org/wiki/Examples_of_Markov_chains

    A game of snakes and ladders or any other game whose moves are determined entirely by dice is a Markov chain, indeed, an absorbing Markov chain. This is in contrast to card games such as blackjack, where the cards represent a 'memory' of the past moves. To see the difference, consider the probability for a certain event in the game.

  5. Stochastic matrix - Wikipedia

    en.wikipedia.org/wiki/Stochastic_matrix

    Intuitively, a stochastic matrix represents a Markov chain; the application of the stochastic matrix to a probability distribution redistributes the probability mass of the original distribution while preserving its total mass. If this process is applied repeatedly, the distribution converges to a stationary distribution for the Markov chain.

  6. 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 π i q i j = π j q j i {\displaystyle \pi _{i}q_{ij}=\pi _{j}q_{ji}} holds, then by summing over j {\displaystyle j} , the global balance equations are satisfied and π {\displaystyle \pi } is the stationary ...

  7. Matrix analytic method - Wikipedia

    en.wikipedia.org/wiki/Matrix_analytic_method

    [1] [2] Such models are often described as M/G/1 type Markov chains because they can describe transitions in an M/G/1 queue. [3] [4] The method is a more complicated version of the matrix geometric method and is the classical solution method for M/G/1 chains. [5]

  8. 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.

  9. Transition-rate matrix - Wikipedia

    en.wikipedia.org/wiki/Transition-rate_matrix

    In probability theory, a transition-rate matrix (also known as a Q-matrix, [1] intensity matrix, [2] or infinitesimal generator matrix [3]) is an array of numbers describing the instantaneous rate at which a continuous-time Markov chain transitions between states.