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  2. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    One method of finding the stationary probability distribution, π, of an ergodic continuous-time Markov chain, Q, is by first finding its embedded Markov chain (EMC). Strictly speaking, the EMC is a regular discrete-time Markov chain, sometimes referred to as a jump process .

  3. Construction of an irreducible Markov chain in the Ising model

    en.wikipedia.org/wiki/Construction_of_an...

    An aperiodic, reversible, and irreducible Markov Chain can then be obtained using Metropolis–Hastings algorithm. Persi Diaconis and Bernd Sturmfels showed that (1) a Markov basis can be defined algebraically as an Ising model [ 2 ] and (2) any generating set for the ideal I := ker ⁡ ( ψ ∗ ϕ ) {\displaystyle I:=\ker({\psi }*{\phi ...

  4. Continuous-time Markov chain - Wikipedia

    en.wikipedia.org/wiki/Continuous-time_Markov_chain

    This Markov chain is irreducible, because the ghosts can fly from every state to every state in a finite amount of time. Due to the secret passageway, the Markov chain is also aperiodic, because the ghosts can move from any state to any state both in an even and in an uneven number of state transitions.

  5. Irreducibility (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Irreducibility_(mathematics)

    A detailed definition is given here. Also, a Markov chain is irreducible if there is a non-zero probability of transitioning (even if in more than one step) from any state to any other state. In the theory of manifolds, an n-manifold is irreducible if any embedded (n − 1)-sphere bounds an embedded n-ball.

  6. 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.

  7. Markov model - Wikipedia

    en.wikipedia.org/wiki/Markov_model

    The simplest Markov model is the Markov chain.It models the state of a system with a random variable that changes through time. In this context, the Markov property indicates that the distribution for this variable depends only on the distribution of a previous state.

  8. 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.

  9. Markov decision process - Wikipedia

    en.wikipedia.org/wiki/Markov_decision_process

    The "Markov" in "Markov decision process" refers to the underlying structure of state transitions that still follow the Markov property. The process is called a "decision process" because it involves making decisions that influence these state transitions, extending the concept of a Markov chain into the realm of decision-making under uncertainty.