论文标题
连续时间，离散事件过程的推论，预测和熵率估计
Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes
论文作者
论文摘要
推断模型，预测未来以及估计离散时间的离散事件过程的熵率是破旧的。但是，一类更广泛的离散事件过程连续运行。在这里，我们提供了推断，预测和估算它们的新方法。这些方法依赖于利用神经网络的通用近似功率的贝叶斯结构推断的扩展。基于具有复杂合成数据的实验，该方法与最新的预测和熵率估计具有竞争力。
Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network's universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.
