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大关键局限（可扩展性和预测性能）的模型。特别地，使用狄利克雷过程作为门控函数的模型能够直观解释并自动选择混合中的专家数量。尽管现有模型直观且能捕捉非平稳性、多模态性和异方差性，但其门控函数的简单性可能限制了在复杂数据生成过程中的预测性能。借助依赖狄利克雷过程研究的最新进展，我们提出了一种基于核粘合断裂过程的高斯过程专家混合新模型。该模型在保持直观吸引力的同时提升了现有模型的性能。为使模型实用化，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，验证了该模型的行为特性与改进的预测性能。