Abstract Number:
3281
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Donald Martin (1), Nonhle Mdziniso (2), Elie Alhajjar (3), Laurent Noe (4)
Institutions:
(1) NC State University, N/A, (2) Rochester Institute of technology, N/A, (3) RAND Corporation, N/A, (4) CRIStAL (UMR 9189 Lille University/CNRS) - INRIA Lille Nord-Europe,, France
Co-Author(s):
Laurent Noe
CRIStAL (UMR 9189 Lille University/CNRS) - INRIA Lille Nord-Europe,
First Author:
Presenting Author:
Abstract Text:
A Markov chain-based approach yields an efficient computation mechanism to compute a single distribution of a pattern statistic in a Markovian sequence. However, if distributions are needed for many values of input probabilities, the entire computation needs to be repeated. The method forwarded in this work avoids the need to redo recursive updates of probabilities. Instead, counts of data strings with various values of sufficient statistics are updated recursively. The final counts are then used to reconstruct probabilities for the many input probabilities, improving efficiency. In this talk, the methodology is laid out systematically.
Keywords:
Markovian data, parameter-free computation, recursive computation| | | | |
Sponsors:
Section on Statistical Computing
Tracks:
Computationally Intensive Methods
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