Fusion of Gaussian Processes Predictions with Monte Carlo Sampling
This work addresses the need for systematic model fusion in Bayesian settings, but it is incremental as it builds on existing pooling techniques.
The paper tackles the problem of integrating multiple Gaussian process model predictions by introducing novel log-linear pooling methods with input-dependent weights, and demonstrates their performance on a synthetic dataset.
In science and engineering, we often work with models designed for accurate prediction of variables of interest. Recognizing that these models are approximations of reality, it becomes desirable to apply multiple models to the same data and integrate their outcomes. In this paper, we operate within the Bayesian paradigm, relying on Gaussian processes as our models. These models generate predictive probability density functions (pdfs), and the objective is to integrate them systematically, employing both linear and log-linear pooling. We introduce novel approaches for log-linear pooling, determining input-dependent weights for the predictive pdfs of the Gaussian processes. The aggregation of the pdfs is realized through Monte Carlo sampling, drawing samples of weights from their posterior. The performance of these methods, as well as those based on linear pooling, is demonstrated using a synthetic dataset.