Abstract
In Chap. 1 we considered Markov chains X n with a discrete time index n = 0, 1, 2, … In this chapter we will extend the notion to a continuous time parameter t ≥ 0, a setting that is more convenient for some applications. In discrete time we formulated the Markov property as: for any possible values of j, i, i n−1, … i 0
In continuous time, it is technically difficult to define the conditional probability given all of the X r for r ≤ s, so we instead say that X t , t ≥ 0 is a Markov chain if for any 0 ≤ s 0 < s 1⋯ < s n < s and possible states i 0, …, i n , i, j we have
In words, given the present state, the rest of the past is irrelevant for predicting the future. Note that built into the definition is the fact that the probability of going from i at time s to j at time s + t only depends on t the difference in the times.
References
Bailey, N.T.J. (1964) The Elements of Stochastic Processes: With Applications to the Natural Sciences. John Wiley and Sons.
Hasegawa, M., Kishino, H., Yano, T. (1985) Dating of human-ape splitting by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution. 22(2):160–174
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Durrett, R. (2016). Continuous Time Markov Chains. In: Essentials of Stochastic Processes. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-45614-0_4
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DOI: https://doi.org/10.1007/978-3-319-45614-0_4
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