Integrated Continuous-time Hidden Markov Models
Integrated Continuous-time Hidden Markov Models
复制标题
集成连续时间隐马尔可夫模型
DOI：
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发表时间：
2018
期刊：
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通讯作者：
P. Blackwell
中科院分区：
文献类型：
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作者：
P. Blackwell
出于运动生态学中的应用，在本文中，我提出了一类新的综合连续时间隐马尔可夫模型，其中每个观察依赖于在整个时间间隔的过程中的底层状态，因为前一个观察，不仅在其当前状态。这个类给出了一系列现有模型的新表示，包括一些广泛应用的开关扩散模型。我表明，在适当的条件下，在这个类中的模型可以被视为一个传统的隐马尔可夫模型，使使用的前向算法有效地评估其可能性，而无需采样其状态序列。这导致了一种推理算法，该算法比现有方法更有效，并且随着数据量的增加而更好地扩展。这是证明和量化的一些应用程序中的动物运动数据和一些相关的模拟实验。
Motivated by applications in movement ecology, in this paper I propose a new class of integrated continuous-time hidden Markov models in which each observation depends on the underlying state of the process over the whole interval since the previous observation, not only on its current state. This class gives a new representation of a range of existing models, including some widely applied switching diffusion models. I show that under appropriate conditioning, a model in this class can be regarded as a conventional hidden Markov model, enabling use of the Forward Algorithm for efficient evaluation of its likelihood without sampling of its state sequence. This leads to an algorithm for inference which is more efficient, and scales better with the amount of data, than existing methods. This is demonstrated and quantified in some applications to animal movement data and some related simulation experiments.