A stochastic memoizer for sequence data
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...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2009
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