Predicting non-stationary processes
- 作者：Daniil Ryabko ; Marcus Hutter
- 关键词：Sequence prediction ; Local absolute continuity ; Non-stationary measures ; Average/expected criteria ; Absolute/KL divergence ; Mixtures of measures
- 刊名：Applied Mathematics Letters
- 出版年：2008
- 期刊代码：5_08939659
- 类别：mt
- 出版时间：May 2008
- 卷：21
- 期：5
- 页码：477-482
- 文件大小：217 K
摘要
Suppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences. Consider the question of when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense), if one of the measures is chosen to generate the sequence. This question may be considered a refinement of the problem of sequence prediction in its most general formulation: for a given class of probability measures, does there exist a measure which predicts all of the measures in the class? To address this problem, we find some conditions on local absolute continuity which are sufficient for prediction and generalize several different notions that are known to be sufficient for prediction. We also formulate some open questions to outline a direction for finding the conditions on classes of measures for which prediction is possible.