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  2. 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.

  3. Markov model - Wikipedia

    en.wikipedia.org/wiki/Markov_model

    Markov model. In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. [1] It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property ). Generally, this assumption enables reasoning and computation with ...

  4. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    A more recent example is the Markov switching multifractal model of Laurent E. Calvet and Adlai J. Fisher, which builds upon the convenience of earlier regime-switching models. [99] [100] It uses an arbitrarily large Markov chain to drive the level of volatility of asset returns.

  5. Markov property - Wikipedia

    en.wikipedia.org/wiki/Markov_property

    The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model. A Markov random field extends this property to two or more dimensions or to random variables defined for an interconnected network of items. An example of a model for such a field is the Ising model.

  6. Markov chain Monte Carlo - Wikipedia

    en.wikipedia.org/wiki/Markov_chain_Monte_Carlo

    In statistics, Markov chain Monte Carlo ( MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it – that is, the Markov chain's equilibrium distribution matches the target distribution.

  7. Baum–Welch algorithm - Wikipedia

    en.wikipedia.org/wiki/Baum–Welch_algorithm

    Baum–Welch algorithm. In electrical engineering, statistical computing and bioinformatics, the Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.

  8. M/G/1 queue - Wikipedia

    en.wikipedia.org/wiki/M/G/1_queue

    M/G/1 queue. In queueing theory, a discipline within the mathematical theory of probability, an M/G/1 queue is a queue model where arrivals are M arkovian (modulated by a Poisson process ), service times have a G eneral distribution and there is a single server. [1] The model name is written in Kendall's notation, and is an extension of the M/M ...

  9. Markov decision process - Wikipedia

    en.wikipedia.org/wiki/Markov_decision_process

    Markov decision process. In mathematics, a Markov decision process ( MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via ...