Multiple Information Source Bayesian Optimization
Multiple Information Source Bayesian Optimization
English | 2025 | ISBN: 3031979648 | 108 Pages | PDF EPUB (True) | 20 MB
English | 2025 | ISBN: 3031979648 | 108 Pages | PDF EPUB (True) | 20 MB
The book provides a comprehensive review of multiple information sources and multi-fidelity Bayesian optimization, specifically focusing on the novel "Augmented Gaussian Process” methodology. The book is important to clarify the relations and the important differences in using multi-fidelity or multiple information source approaches for solving real-world problems. Choosing the most appropriate strategy, depending on the specific problem features, ensures the success of the final solution. The book also offers an overview of available software tools: in particular it presents two implementations of the Augmented Gaussian Process-based Multiple Information Source Bayesian Optimization, one in Python – and available as a development branch in BoTorch – and finally, a comparative analysis against other available multi-fidelity and multiple information sources optimization tools is presented, considering both test problems and real-world applications.