Abstract Number:
2870
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Donald Martin (1), Laurent Noe (2), Elie Alhajjar (3), Nonhle Mdziniso (4)
Institutions:
(1) NC State University, Raleigh, NC, (2) CRIStAL (UMR 9189 Lille University/CNRS) - INRIA Lille Nord-Europe, France, (3) RAND Corporation, Arlington, VA, (4) Rochester Institute of Technology, Rochester, NY
Co-Author(s):
Laurent Noe
CRIStAL (UMR 9189 Lille University/CNRS) - INRIA Lille Nord-Europe
First Author:
Presenting Author:
Abstract Text:
Auxiliary Markov chains have been used as a mechanism to efficiently compute the distribution of a pattern statistic in a Markovian sequence. However, if distributions are needed for many values of input probabilities and not just one set of values, the entire computation needs to be repeated. In this work, a method is forwarded that reduces computational burden for this scenario. Counts of data strings with various values of sufficient statistics are updated instead of probabilities. The final counts are then used to reconstruct probabilities for the many input probabilities, improving efficiency. In this talk, the methodology is illustrated on computing the probability of accepting a unit in start-up demonstration tests for many different start-up probabilities.
Keywords:
minimal deterministic finite automaton|sequence alignment|sequential computation|spaced seeds|sparse Markov models|start-up demonstration tests
Sponsors:
Section on Statistical Computing
Tracks:
Miscellaneous
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