Context tree weighting

The context tree weighting method (CTW) is a lossless compression and prediction algorithm by {{harvnb|Willems|Shtarkov|Tjalkens|1995}}. The CTW algorithm is among the very few such algorithms that offer both theoretical guarantees and good practical performance (see, e.g. {{harvnb|Begleiter|El-Yaniv|Yona|2004}}).

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

  • {{Citation

| last1=Willems

| last2=Shtarkov

| last3=Tjalkens

| year=1995

| title=The Context-Tree Weighting Method: Basic Properties

| journal=IEEE Transactions on Information Theory

| publication-place=IEEE Transactions on Information Theory

| volume=41

| number=3

| pages=653–664

| doi=10.1109/18.382012

| url=https://ieeexplore.ieee.org/document/382012

}}

  • {{Citation

| last1=Willems

| last2=Shtarkov

| last3=Tjalkens

| year=1997

| title=Reflections on "The Context-Tree Weighting Method: Basic Properties"

| publication-place=IEEE Information Theory Society Newsletter

| volume=47

| number=1

| citeseerx=10.1.1.109.1872

}}

  • {{Citation

| last1=Begleiter

| last2=El-Yaniv

| last3=Yona

| year=2004

| title=On Prediction Using Variable Order Markov Models

| publication-place=Journal of Artificial Intelligence Research

| volume=22

| pages=385–421

| url=https://www.jair.org/index.php/jair/article/view/10394

| journal=Journal of Artificial Intelligence Research

| doi=10.1613/jair.1491

| s2cid=47180476

| arxiv=1107.0051

}}