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  2. Markov property - Wikipedia

    en.wikipedia.org/wiki/Markov_property

    The term strong Markov property is similar to the Markov property, except that the meaning of "present" is defined in terms of a random variable known as a stopping time. The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model .

  3. Memorylessness - Wikipedia

    en.wikipedia.org/wiki/Memorylessness

    The memorylessness property asserts that the number of previously failed trials has no effect on the number of future trials needed for a success. Geometric random variables can also be defined as taking values in N 0 {\displaystyle \mathbb {N} _{0}} , which describes the number of failed trials before the first success in a sequence of ...

  4. History of network traffic models - Wikipedia

    en.wikipedia.org/wiki/History_of_network_traffic...

    The compound Poisson model shares some of the analytical benefits of the pure Poisson model: the model is still memoryless, aggregation of streams is still (compound) Poisson, and the steady-state equation is still reasonably simple to calculate, although varying batch parameters for differing flows would complicate the derivation.

  5. Markov chain - Wikipedia

    en.wikipedia.org/wiki/Markov_chain

    Formally, the steps are the integers or natural numbers, and the random process is a mapping of these to states. The Markov property states that the conditional probability distribution for the system at the next step (and in fact at all future steps) depends only on the current state of the system, and not additionally on the state of the ...

  6. Survival function - Wikipedia

    en.wikipedia.org/wiki/Survival_function

    For an exponential survival distribution, the probability of failure is the same in every time interval, no matter the age of the individual or device. This fact leads to the "memoryless" property of the exponential survival distribution: the age of a subject has no effect on the probability of failure in the next time interval.

  7. Examples of Markov chains - Wikipedia

    en.wikipedia.org/wiki/Examples_of_Markov_chains

    The next state of the board depends on the current state, and the next roll of the dice. It does not depend on how things got to their current state. In a game such as blackjack, a player can gain an advantage by remembering which cards have already been shown (and hence which cards are no longer in the deck), so the next state (or hand) of the ...

  8. A judge in Brazil has ordered Adele’s song Million Years Ago to be removed globally from streaming services due to a plagiarism claim by Brazilian composer, Toninho Geraes. Geraes alleges that ...

  9. Phase-type distribution - Wikipedia

    en.wikipedia.org/wiki/Phase-type_distribution

    Consider a continuous-time Markov process with m + 1 states, where m ≥ 1, such that the states 1,...,m are transient states and state 0 is an absorbing state. Further, let the process have an initial probability of starting in any of the m + 1 phases given by the probability vector (α 0,α) where α 0 is a scalar and α is a 1 × m vector.