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English(EN) Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models

新的张量化算法改进了因子隐马尔可夫模型的分析

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了新的张量化算法和可扩展滤波方法，以解决因子隐马尔可夫模型（fHMMs）相关的计算挑战。这些模型对于具有多个独立因子的系统来说更现实，但由于其状态空间增大，在被重新表述为标准HMM时会增加计算成本。所提出的方法利用张量代数直接利用fHMMs的多维结构，绕过了中间HMM表示的需要。这种新颖的滤波方法显著提高了计算性能，使得大型系统和数据集的分析更加高效和实用。 AI

影响 fHMM分析的这些进步可以实现对复杂时间序列数据更有效的处理，可能影响那些依赖于多因子系统复杂建模的领域。

排序理由 该集群包含一篇学术论文，详细介绍了用于特定类型统计模型的新算法和方法。

在 arXiv stat.ML 阅读 →

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新的张量化算法改进了因子隐马尔可夫模型的分析

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Roxana Barrios, Ioannis Sgouralis ·

    用于隐马尔可夫和因子隐马尔可夫模型的张量化算法和可扩展滤波方法

    arXiv:2607.07008v1 Announce Type: new Abstract: A common method for the representation and analysis of time-series data is the hidden Markov model (HMM), where each observation is associated with a hidden state that evolves over time. However, many real-world systems are influenc…

  2. arXiv stat.ML TIER_1 English(EN) · Ioannis Sgouralis ·

    用于隐马尔可夫和因子隐马尔可夫模型的张量化算法和可扩展滤波方法

    A common method for the representation and analysis of time-series data is the hidden Markov model (HMM), where each observation is associated with a hidden state that evolves over time. However, many real-world systems are influenced by multiple independent factors, which are mo…

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