Fusion of Gaussian Processes Predictions with Monte Carlo Sampling

arXiv:2403.01389v1 Announce Type: new
Abstract: 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 …

abstract apply arxiv cs.lg data engineering fusion gaussian processes multiple paper prediction predictions processes reality sampling science stat.ml type variables work