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Probabilistic models for pattern statistics

Published online by Cambridge University Press:  20 July 2006

Massimiliano Goldwurm  and
Roberto Radicioni
Massimiliano Goldwurm
Affiliation:
Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano, Italy; goldwurm@dsi.unimi.it & radicion@dsi.unimi.it.
Roberto Radicioni
Affiliation:
Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano, Italy; goldwurm@dsi.unimi.it & radicion@dsi.unimi.it.
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Abstract

In this work we study some probabilistic models for the random generation of words over a given alphabet used in the literature in connection with pattern statistics. Our goal is to compare models based on Markovian processes (where the occurrence of a symbol in a given position only depends on a finite number of previous occurrences) and the stochastic models that can generate a word of given length from a regular language under uniform distribution. We present some results that show the differences between these two stochastic models and their relationship with the rational probabilistic measures.

Keywords

Pattern statisticsMarkov chainsprobabilistic automatarational formal series.
Type
Research Article
Information
RAIRO - Theoretical Informatics and Applications , Volume 40 , Issue 2: Alberto Bertoni: Climbing summits , April 2006 , pp. 207 - 225
Copyright
© EDP Sciences, 2006

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