The study focuses on integrating multiple Gaussian process models for improved predictions. Log-linear pooling and Bayesian hierarchical stacking are introduced as novel approaches. The performance is evaluated through experiments on synthetic datasets, showcasing the effectiveness of the proposed methods.
The paper discusses the importance of combining predictive probability density functions generated by Gaussian processes. It highlights the challenges in determining reliable estimates from an ensemble of GPs and introduces innovative strategies for fusion. The use of Monte Carlo sampling to aggregate predictive pdfs is demonstrated, emphasizing the significance of model ensembles in enhancing predictive performance.
Different methods like stacking, Bayesian hierarchical stacking, and mixture of GP experts are compared in terms of their ability to improve predictive power. The study delves into the mathematical foundations behind log-linear pooling and its implications for creating more flexible and robust models. Experimental results show that log-linear pooling outperforms traditional linear pooling methods in certain scenarios.
Overall, the research provides valuable insights into how Gaussian process fusion can be optimized through innovative techniques like log-linear pooling and Monte Carlo sampling. By exploring various fusion strategies and conducting numerical comparisons, the authors shed light on effective ways to integrate multiple models for enhanced predictive accuracy.
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by Marzieh Ajir... kl. arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01389.pdfDybere Forespørgsler