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A (finite) drunkard's walk is an example of an absorbing Markov chain. [1] In the mathematical theory of probability, an absorbing Markov chain is a Markov chain in which every state can reach an absorbing state. An absorbing state is a state that, once entered, cannot be left.
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.
A Markov chain is a type of Markov process that has either a discrete state space or a discrete index set (often representing time), but the precise definition of a Markov chain varies. [6] For example, it is common to define a Markov chain as a Markov process in either discrete or continuous time with a countable state space (thus regardless ...
The distribution can be represented by a random variable describing the time until absorption of an absorbing Markov chain with one absorbing state. Each of the states of the Markov chain represents one of the phases. It has continuous time equivalent in the phase-type distribution.
Markov additive process; Markov blanket; Markov chain. Markov chain geostatistics; Markov chain mixing time; Markov chain Monte Carlo; Markov decision process; Markov information source; Markov kernel; Markov logic network; Markov model; Markov network; Markov process; Markov property; Markov random field; Markov renewal process; Markov's ...
Fundamental matrix (absorbing Markov chain) This page was last edited on 27 February 2022, at 15:25 (UTC). Text is available under the Creative Commons Attribution ...
The IEA expects world oil demand growth to accelerate next year, with consumption rising to 1.1 million barrels per day next year — but that's not enough to absorb the oversupply.
This category is for articles about the theory of Markov chains and processes, and associated processes. See Category:Markov models for models for specific applications that make use of Markov processes.