Communicating Classes in Markov Chains

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Resources discussing communicating classes in Markov chains highlight methods for treating these classes as "coarse grained" Markov chains. Transition rates between the classes can be computed using the concept of lumpable Markov chains, as defined in the referenced PDF. This terminology may vary, but the principles remain relevant for analyzing Markov chains. The discussion emphasizes the importance of understanding these concepts for effective Markov chain analysis. Overall, the exploration of coarse-grained methods is crucial for advancing the study of Markov processes.
Arsenic&Lace
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Does anybody know of a good resource which might discuss these in greater detail? In particular, once one has reduced a Markov chain to a set of communicating classes, are there methods to treat this as a "coarse grained" Markov chain for which one can then compute transition rates between the classes?

Thanks!
 
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Nice find, thanks!
 
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