Detecting Renewal States in Chains of Variable Length via Intrinsic Bayes Factors. (arXiv:2110.07430v2 [cs.LG] UPDATED)

Markov chains with variable length are useful parsimonious stochastic models
able to generate most stationary sequence of discrete symbols. The idea is to
identify the suffixes of the past, called contexts, that are relevant to
predict the future symbol. Sometimes a single state is a context, and looking
at the past and finding this specific state makes the further past irrelevant.
States with such property are called renewal states and they can be used to
split the chain into independent …

arxiv bayes