Abstract
A Markov process is a dynamical system where movement within the system consists of transitions between a finite set of states. These transitions are governed by prescribed probabilistic rules and are memoryless in the sense that the transition to a new state depends only on the current state of the system and not on the entire transition history. We consider only finite-state, discrete-time Markov processes.
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References
Isaacson, Dean L., and Richard W. Madsen, Markov Chains: Theory and Applications, John Wiley & Sons, New York, 1976.
Noble, Safiya Umoja, Algorithms of Oppression: How Search Engines Reinforce Racism, New York University Press, New York, 2018.
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Feeman, T.G. (2023). Markov Processes. In: Applied Linear Algebra and Matrix Methods. Springer Undergraduate Texts in Mathematics and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-39562-8_8
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DOI: https://doi.org/10.1007/978-3-031-39562-8_8
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Online ISBN: 978-3-031-39562-8
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