Abstract
Besides independent random variables (r.v.’s), modern probability also studies so-called weakly dependent r.v.’s. This is related both to the logical growth of probability theory and to its specific applications. Among the various weak dependency schemes, a special place is occupied by the mixing homogeneous Markov chains. They serve like an intermediary link between independent and identically distributed r.v.’s and the more complicated weak dependency schemes. In addition, homogeneous Markov chains have a fairly simple structure and are therefore comparatively easy to investigate. Finally, they are good models of many processes observed in practice.
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Gudynas, P. (2000). Refinements of the Central Limit Theorem for Homogeneous Markov Chains. In: Prokhorov, Y.V., Statulevičius, V. (eds) Limit Theorems of Probability Theory. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04172-7_4
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DOI: https://doi.org/10.1007/978-3-662-04172-7_4
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