ON THE FIRST k MOMENTS OF THE RANDOM COUNT OF A PATTERN IN A MULTISTATE SEQUENCE GENERATED BY A MARKOV SOURCE
نویسنده
چکیده
In this paper we develop an explicit formula that allows us to compute the first k moments of the random count of a pattern in a multistate sequence generated by aMarkov source. We derive efficient algorithms that allow us to deal with any pattern (low or high complexity) in any Markov model (homogeneous or not). We then apply these results to the distribution of DNA patterns in genomic sequences, and we show that moment-based developments (namely Edgeworth’s expansion and Gram–Charlier type-B series) allow us to improve the reliability of common asymptotic approximations, such as Gaussian or Poisson approximations.
منابع مشابه
On the first k moments of the random count of a pattern in a multi-states sequence generated by a Markov source
In this paper, we develop an explicit formula allowing to compute the first k moments of the random count of a pattern in a multi-states sequence generated by a Markov source. We derive efficient algorithms allowing to deal both with low or high complexity patterns and either homogeneous or heterogenous Markov models. We then apply these results to the distribution of DNA patterns in genomic se...
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