LEADER 03406nam 22006015 450 001 9911022454403321 005 20250831130224.0 010 $a3-031-97965-6 024 7 $a10.1007/978-3-031-97965-1 035 $a(CKB)40851809500041 035 $a(MiAaPQ)EBC32275511 035 $a(Au-PeEL)EBL32275511 035 $a(DE-He213)978-3-031-97965-1 035 $a(EXLCZ)9940851809500041 100 $a20250831d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultiple Information Source Bayesian Optimization /$fby Antonio Candelieri, Andrea Ponti, Francesco Archetti 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (169 pages) 225 1 $aSpringerBriefs in Optimization,$x2191-575X 311 08$a3-031-97964-8 327 $aPreface -- Introduction -- MISO-AGP: dealing with multiple information sources via Augmented Gaussian Process -- MISO-AGP in action: selected applications -- Bayesian Optimization and Large Language Models -- References. 330 $aThe book provides a comprehensive review of multiple information sources and multi-fidelity Bayesian optimization, specifically focusing on the novel "Augmented Gaussian Process? methodology. The book is important to clarify the relations and the important differences in using multi-fidelity or multiple information source approaches for solving real-world problems. Choosing the most appropriate strategy, depending on the specific problem features, ensures the success of the final solution. The book also offers an overview of available software tools: in particular it presents two implementations of the Augmented Gaussian Process-based Multiple Information Source Bayesian Optimization, one in Python -- and available as a development branch in BoTorch -- and finally, a comparative analysis against other available multi-fidelity and multiple information sources optimization tools is presented, considering both test problems and real-world applications. The book will be useful to two main audiences: 1. PhD candidates in Computer Science, Artificial Intelligence, Machine Learning, and Optimization 2. Researchers from academia and industry who want to implement effective and efficient procedures for designing experiments and optimizing computationally expensive experiments in domains like engineering design, material science, and biotechnology. . 410 0$aSpringerBriefs in Optimization,$x2191-575X 606 $aMathematical optimization 606 $aStatistics 606 $aMachine learning 606 $aOptimization 606 $aBayesian Inference 606 $aMachine Learning 615 0$aMathematical optimization. 615 0$aStatistics. 615 0$aMachine learning. 615 14$aOptimization. 615 24$aBayesian Inference. 615 24$aMachine Learning. 676 $a519.6 700 $aCandelieri$b Antonio$0781001 701 $aPonti$b Andrea$0724138 701 $aArchetti$b Francesco$060966 701 $aSabatella$b Antonio$01846894 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911022454403321 996 $aMultiple Information Source Bayesian Optimization$94431789 997 $aUNINA