Schemes for Bidirectional Modeling of Discrete Stationary Sources

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