Mixtures of Gaussian process experts is a class of models that can simultaneously address two of the key limitations inherent in standard Gaussian processes: scalability and predictive performance. In particular, models that use Dirichlet processes as gating functions permit straightforward interpretation and automatic selection of the number of experts in a mixture. While the existing models are intuitive and capable of capturing non-stationarity, multi-modality and heteroskedasticity, the simplicity of their gating functions may limit the predictive performance when applied to complex data-generating processes. Capitalising on the recent advancement in the dependent Dirichlet processes literature, we propose a new mixture model of Gaussian process experts based on kernel stick-breaking processes. Our model maintains the intuitive appeal yet improve the performance of the existing models. To make it practical, we design a sampler for posterior computation based on the slice sampling. The model behaviour and improved predictive performance are demonstrated in experiments using six datasets.

翻译：高斯过程专家混合模型是一类能够同时解决标准高斯过程中两个关键局限性的模型：可扩展性和预测性能。特别地，使用狄利克雷过程作为门控函数的模型允许直观的解释和自动选择混合中的专家数量。尽管现有模型直观且能够捕捉非平稳性、多模态性和异方差性，但其门控函数的简单性可能限制了对复杂数据生成过程的预测性能。利用依赖狄利克雷过程文献中的最新进展，我们提出了一种基于核stick-breaking过程的高斯过程专家混合新模型。该模型保持了现有模型的直观吸引力，同时提升了其性能。为使其实用化，我们基于切片采样设计了后验计算的采样器。通过六个数据集的实验，展示了模型行为和改进的预测性能。