Jiming Yu

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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)
—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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