Abstract
We develop adaptive schemes for bidirectional modeling of unknown discrete stationary sources. These algorithms can be applied to statistical inference problems such as noncausal universal discrete denoising that exploit bidirectional dependencies. Efficient algorithms for constructing those models are developed and we compare their performance to that of the DUDE algorithm for universal discrete denoising.
Original language | English (US) |
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Pages (from-to) | 4789-4807 |
Number of pages | 19 |
Journal | IEEE Transactions on Information Theory |
Volume | 52 |
Issue number | 11 |
DOIs | |
State | Published - Nov 2006 |
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences
Keywords
- Bidirectional modeling
- Discrete stationary sources
- Universal algorithms
- Universal discrete denoising