Source: J. Appl. Probab. Volume 45, Number 1
(2008), 226-243.
In the framework of patterns in random texts, the Markov chain
embedding techniques consist of turning the occurrences of a
pattern over an order-m Markov sequence into those of a
subset of states into an order-1 Markov chain. In this paper we
use the theory of language and automata to provide space-optimal
Markov chain embedding using the new notion of pattern Markov
chains (PMCs), and we give explicit constructive algorithms to
build the PMC associated to any given pattern problem. The
interest of PMCs is then illustrated through the exact computation
of P-values whose complexity is discussed and compared to other
classical asymptotic approximations. Finally, we consider two
illustrative examples of highly degenerated pattern problems
(structured motifs and PROSITE signatures), which further
illustrate the usefulness of our approach.
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