On universal algorithms for classifying and predicting stationary processes

IF 1.3 Q2 STATISTICS & PROBABILITY
G. Morvai, B. Weiss
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引用次数: 10

Abstract

This is a survey of results on universal algorithms for classification and prediction of stationary processes. The classification problems include discovering the order of a k-step Markov chain, determining memory words in finitarily Markovian processes and estimating the entropy of an unknown process. The prediction problems cover both discrete and real valued processes in a variety of situations. Both the forward and the backward prediction problems are discussed with the emphasis being on pointwise results. This survey is just a teaser. The purpose is merely to call attention to results on classification and prediction. We will refer the interested reader to the sources. Throughout the paper we will give illuminating examples. AMS 2000 subject classifications: Primary 60G25, 60G10.
平稳过程分类与预测的通用算法
这是对平稳过程分类和预测的通用算法结果的调查。分类问题包括发现k步马尔可夫链的阶数、确定有限马尔可夫过程中的记忆词以及估计未知过程的熵。预测问题涵盖了各种情况下的离散和实值过程。讨论了前向和后向预测问题,重点讨论了逐点结果。这个调查只是一个引子。其目的仅仅是为了引起人们对分类和预测结果的注意。我们将向感兴趣的读者介绍资料。在整篇论文中,我们将给出有启发性的例子。AMS 2000学科分类:初级60G25、初级60G10。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Probability Surveys
Probability Surveys STATISTICS & PROBABILITY-
CiteScore
4.70
自引率
0.00%
发文量
9
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