Statistical Analysis of Big Data Based on Parsimonious Models of High-Order Markov Chains

机译：基于高阶马尔可夫链简约模型的大数据统计分析

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摘要

The paper is devoted to construction of parsimonious (small-parametric) models of high-order Markov chains and to computer algorithms for statistical inferences on parameters of these models.

机译：本文致力于构建高阶马尔可夫链的简约（小参数）模型，以及用于对这些模型的参数进行统计推断的计算机算法。

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来源
《Analytical and computational methods in probability theory》|2017年|485-496|共12页
会议地点 Moscow(RU)
作者
Yu. S. Kharin;
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作者单位

Research Institute for Applied Problems of Mathematics and Informatics, Belarusian State University, 220030 Minsk, Belarus;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
High-Order Markov chain; Parsimonious model; Statistical analysis; Big data; Computer algorithms;

机译：高阶马尔可夫链；简约模型；统计分析;大数据;计算机算法;

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3. Two-State Imprecise Markov Chains for Statistical Modelling of Two-State Non-Markovian Processes [J] . Matthias Troffaes, Thomas Krak, Henna Bains JMLR: Workshop and Conference Proceedings . 2019,第2期

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4. Statistical Analysis of Big Data Based on Parsimonious Models of High-Order Markov Chains [C] . Yu. S. Kharin International Conference on Analytical and Computational Methods in Probability Theory . 2018

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6. Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains [O] . Li C. Xia, Dongmei Ai, Jacob A. Cram, 2015

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Statistical Analysis of Big Data Based on Parsimonious Models of High-Order Markov Chains

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