Faster exact distributions of pattern ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Faster exact distributions of pattern statistics through sequential elimination of states
Auteur(s) :
Martin, Donald [Auteur]
Department of Statistics [Raleigh]
Noé, Laurent [Auteur]
Bioinformatics and Sequence Analysis [BONSAI]
Department of Statistics [Raleigh]
Noé, Laurent [Auteur]
Bioinformatics and Sequence Analysis [BONSAI]
Titre de la revue :
Annals of the Institute of Statistical Mathematics
Pagination :
231--248
Éditeur :
Springer Verlag
Date de publication :
2017-02
ISSN :
0020-3157
Discipline(s) HAL :
Informatique [cs]
Sciences du Vivant [q-bio]
Mathématiques [math]/Probabilités [math.PR]
Informatique [cs]/Bio-informatique [q-bio.QM]
Sciences du Vivant [q-bio]
Mathématiques [math]/Probabilités [math.PR]
Informatique [cs]/Bio-informatique [q-bio.QM]
Résumé en anglais : [en]
When using an auxiliary Markov chain (AMC) to compute sampling distributions, the computational complexity is directly related to the number of Markov chain states. For certain complex pattern statistics, minimal deterministic ...
Lire la suite >When using an auxiliary Markov chain (AMC) to compute sampling distributions, the computational complexity is directly related to the number of Markov chain states. For certain complex pattern statistics, minimal deterministic finite automata (DFA) have been used to facilitate efficient computation by reducing the number of AMC states. For example, when statistics of overlapping pattern occurrences are counted differently than non-overlapping occurrences, a DFA consisting of prefixes of patterns extended to overlapping occurrences has been generated and then minimized to form an AMC. However, there are situations where forming such a DFA is computationally expensive, e.g., with computing the distribution of spaced seed coverage. In dealing with this problem, we develop a method to obtain a small set of states during the state generation process without forming a DFA, and show that a huge reduction in the size of the AMC can be attained.Lire moins >
Lire la suite >When using an auxiliary Markov chain (AMC) to compute sampling distributions, the computational complexity is directly related to the number of Markov chain states. For certain complex pattern statistics, minimal deterministic finite automata (DFA) have been used to facilitate efficient computation by reducing the number of AMC states. For example, when statistics of overlapping pattern occurrences are counted differently than non-overlapping occurrences, a DFA consisting of prefixes of patterns extended to overlapping occurrences has been generated and then minimized to form an AMC. However, there are situations where forming such a DFA is computationally expensive, e.g., with computing the distribution of spaced seed coverage. In dealing with this problem, we develop a method to obtain a small set of states during the state generation process without forming a DFA, and show that a huge reduction in the size of the AMC can be attained.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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