论文标题
基于贝叶斯样条的基于贝叶斯的隐藏马尔可夫模型，该模型具有应用程序数据和睡眠分析的应用
Bayesian spline-based hidden Markov models with applications to actimetry data and sleep analysis
论文作者
论文摘要
基于B-Spline的隐藏Markov模型采用B-Spline来指定发射分布，与传统的参数HMM相比，为数据提供了更灵活的建模方法。我们引入了一个用于推理的贝叶斯框架，从而可以同时估计所有未知模型参数，包括状态数量。通过使用跨维的马尔可夫链采样算法来识别B型平台的简约结构型，而在平行采样框架内的边际可能性中，可以根据边际可能性执行有关状态数量的模型选择。利用广泛的模拟研究，我们证明了我们方法的优越性，而不是替代方法及其鲁棒性和可扩展性。我们说明了我们方法用于动物活动的数据的探索性用途，即Whitetip-Sharks。我们贝叶斯方法的灵活性也促进了更现实的假设的融合，我们通过开发一种新型的层次条件HMM来分析人类活动的昼夜节律和睡眠模型来证明这一点。
B-spline-based hidden Markov models employ B-splines to specify the emission distributions, offering a more flexible modelling approach to data than conventional parametric HMMs. We introduce a Bayesian framework for inference, enabling the simultaneous estimation of all unknown model parameters including the number of states. A parsimonious knot configuration of the B-splines is identified by the use of a trans-dimensional Markov chain sampling algorithm, while model selection regarding the number of states can be performed based on the marginal likelihood within a parallel sampling framework. Using extensive simulation studies, we demonstrate the superiority of our methodology over alternative approaches as well as its robustness and scalability. We illustrate the explorative use of our methods for data on activity in animals, i.e. whitetip-sharks. The flexibility of our Bayesian approach also facilitates the incorporation of more realistic assumptions and we demonstrate this by developing a novel hierarchical conditional HMM to analyse human activity for circadian and sleep modelling.
