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—We develop adaptive schemes for bidirectional mod-eling 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… (More)

- Peter Carr, Jiming Yu, Morgan Stanley, Carr Yu, Ross Recovery
- 2012

- Jiming Yu, Sergio Verdú
- 2006

— Erasure entropy rate (introduced recently by Verdú and Weissman) differs from Shannon's entropy rate in that the conditioning occurs with respect to both the past and the future, as opposed to only the past (or the future). In this paper, universal algorithms for estimating erasure entropy rate are proposed based on the basic and extended context-tree… (More)

- Yihong Wu, I Thank, Prof Sanjeev Kulkarni, Paul, Robert Calderbank, C Erhan +33 others
- 2011

Compressed sensing is a signal processing technique to encode analog sources by real numbers rather than bits, dealing with efficient recovery of a real vector from the information provided by linear measurements. By leveraging the prior knowledge of the signal structure (e.g., sparsity) and designing efficient non-linear reconstruction algorithms,… (More)

—Erasure entropy rate differs from Shannon's entropy rate in that the conditioning occurs with respect to both the past and the future, as opposed to only the past (or the future). In this paper, consistent universal algorithms for estimating era-sure entropy rate are proposed based on the basic and extended context-tree weighting (CTW) algorithms.… (More)

—A source X X X goes through an erasure channel whose output is Z Z Z. The goal is to compress losslessly X X X when the com-pressor knows X X X and Z Z Z and the decompressor knows Z Z Z. We propose a universal algorithm based on context-tree weighting (CTW), parameterized by a memory-length parameter`. We show that if the erasure channel is stationary and… (More)

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