论文标题
自动回旋不对称的线性高斯隐藏马尔可夫模型
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
论文作者
论文摘要
在随着时间的流逝的现实生活过程中，其相关变量之间的关系可能会发生变化。因此，对于每个过程的每个状态，具有不同的推理模型是有利的。不对称的隐藏马尔可夫模型满足了这一动态要求，并提供了一个框架，其中该过程的趋势可以表示为潜在变量。在本文中，我们将这些最近的不对称隐藏模型修改为具有非对称自动回归组件，从而使该模型可以选择自动估计的顺序，从而最大程度地提高了其对给定训练集的惩罚可能性。此外，我们展示了必须调整推理，隐藏状态解码和参数学习以适应所提出的模型。最后，我们使用合成数据和真实数据进行实验，以显示该新模型的功能。
In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model.
