The mixture of Gaussian processes is effective for regression, but it cannot handle the non-
stationary curve clustering problem well. The two-layer mixture of Gaussian process …

Abstract The Mixture of Gaussian Processes (MGP) is a powerful statistical model for
characterizing multimodal data, but its conventional Expectation-Maximization (EM) …

The mixture of Gaussian processes (GPs) is capable of learning any general stochastic
process based on a given set of (sample) curves for the regression and prediction problems …

The Gaussian process is a powerful statistical learning model and has been applied widely
in nonlinear regression and classification. However, it fails to model multi-modal data from a …

The mixture of experts (ME) model is effective for multimodal data in statistics and machine
learning. To treat non-stationary probabilistic regression, the mixture of Gaussian processes …

The mixture of Gaussian processes (MGP) is a powerful and widely used model in machine
learning. However, it remains a challenging problem to determine the actual number of GP …

Student-t mixture of Gaussian processes (TMGP) extends the conventional mixture of
Gaussian processes (MGP) by using Student-t mixture as the input distribution instead of …

高斯过程(GP) 模型是核学习方法与贝叶斯推理相结合的典范, 现已成为机器学习领域的一个
研究热点. 作为对GP 模型的拓展, 高斯过程混合(MGP) 模型具有更强大的学习能力和适应性 …

Data in various research fields can be gathered as repeated measure curves. Although they
consist of finite points, it is usually valuable to consider them as sample curves of stochastic …

Repetition measurements from different sources often occur in data analysis which need to
be model and keep track of the original sources. Moreover, data are usually collected as …