Mixtures of Gaussian process experts based on kernel stick-breaking processes

• URL: http://arxiv.org/abs/2304.13833v2
• Date: Fri, 5 May 2023 21:00:46 GMT
• Title: Mixtures of Gaussian process experts based on kernel stick-breaking processes
• Authors: Yuji Saikai and Khue-Dung Dang
• Abstract summary: 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. The model behaviour and improved predictive performance are demonstrated in experiments using six datasets.
• Score: 0.6396288020763143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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