Context tree weighting

The context tree weighting method (CTW) is a lossless compression and prediction algorithm by . The CTW algorithm is among the very few such algorithms that offer both theoretical guarantees and good practical performance (see, e.g. ). The CTW algorithm is an “ensemble method”, mixing the predictions of many underlying variable order Markov models, where each such model is constructed using zero-order conditional probability estimators.

References

• Willems; Shtarkov; Tjalkens (1995), "The Context-Tree Weighting Method: Basic Properties", IEEE Transactions on Information Theory, 41 (3), IEEE Transactions on Information Theory: 653–664, Bibcode:1995ITIT...41..653W, doi:10.1109/18.382012
• Willems; Shtarkov; Tjalkens (1997), Reflections on "The Context-Tree Weighting Method: Basic Properties", vol. 47, IEEE Information Theory Society Newsletter, CiteSeerX 10.1.1.109.1872{{citation}}: CS1 maint: location missing publisher (link)
• Begleiter; El-Yaniv; Yona (2004), "On Prediction Using Variable Order Markov Models", Journal of Artificial Intelligence Research, 22, Journal of Artificial Intelligence Research: 385–421, arXiv:1107.0051, doi:10.1613/jair.1491, S2CID 47180476

External links

• Relevant CTW papers and implementations
• CTW Official Homepage

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