用于隐马尔可夫模型高效推理的减方差随机优化技术

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-05-07 DOI:10.1080/10618600.2024.2350476

Evan Sidrow, Nancy Heckman, Alexandre Bouchard-Côté, Sarah M. E. Fortune, Andrew W. Trites, Marie Auger-Méthé

引用次数: 0

摘要

隐马尔可夫模型（HMMs）是从连续数据中识别有限数量潜状态的常用模型。然而，将它们拟合到大型数据集可能对计算能力要求很高，因为这些数据集的潜在状态数量有限。

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

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.