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A stochastic memoizer for sequence data

Abstract:

We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares statistical strength between subsequent symbol predictive distributions in such a way that predictive performance generalizes well. The model builds on a specific parameterization of an unbounded-depth hierarchical Pitman-Yor process. We introduce analytic marginalization steps (using coagulation operators) to reduce this...

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Publisher copy:
10.1145/1553374.1553518
Host title:
ACM International Conference Proceeding Series
Volume:
382
Publication date:
2009-01-01
DOI:
ISBN:
9781605585161
Pubs id:
pubs:353246
UUID:
uuid:c9e1ad1f-c461-4f98-9713-ec365e6b281a
Local pid:
pubs:353246
Source identifiers:
353246
Deposit date:
2013-11-16

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