LEADER 03109nam 2200373z- 450 001 9910688320603321 005 20230221133405.0 035 $a(CKB)5400000000041911 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/75009.2 035 $a(EXLCZ)995400000000041911 100 $a20202112d2019 |y 0 101 0 $aspa 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLos cazadores-recolectores y las plantas en Patagonia$ePerspectivas desde el sitio cueva Baño Nuevo 1, Aisén 210 $aSantiago, Chile$cSocial-ediciones$d2019 311 $a956-19-1137-X 330 $aThe Baño Nuevo 1 cave is a unique and relevant archaeological site for the study of the regional prehistory of Aisén, from the Patagonia and the South American region, particularly for the set of human burials deposited during the Early Holocene and its long occupational sequence. In this study, the significance of the archaeobotanical record is assessed of Baño Nuevo 1, from a combination of the macro and microscopic lines of evidence, highlighting its contribution to archaeological information discussing plant supply strategies and their uses among the steppe hunter-gatherer groups settled in the place for more than seven thousand five hundred years. Carolina Belmar, Archaeologist specialized in Archeobotany, presents her new book, in which she addresses the Uses given to plants by hunter-gatherer groups settled for more than 7,500 years in the Baño Nuevo 1 cave in the Aisén region, Chile. 330 $aLa cueva Baño Nuevo 1 es un sitio arqueológico único y relevante para el estudio de la prehistoria regional de Aisén, de la Patagonia y de la región Sudamericana, particularmente por el conjunto de entierros humanos depositados durante el Holoceno temprano y su larga secuencia ocupacional. En este estudio se valora lo significativo del registro arqueobotánico de Baño Nuevo 1, a partir de una combinación de las líneas de evidencia macro y microscópica, destacando su contribución a la información arqueológica discutiendo las estrategias de aprovisionamiento de las plantas y sus usos entre los grupos cazadores-recolectores esteparios asentados en el lugar a lo largo de más de siete mil quinientos años. Carolina Belmar, Arqueóloga especialista en Arqueobotánica, presenta su nuevo libro, en el que aborda los usos que dieron a las plantas los grupos cazadores-recolectores asentados durante más de 7500 años en la cueva de Baño Nuevo 1 en la región de Aisén, Chile. 517 $aCazadores-recolectores y las plantas en Patagonia. 606 $aPrehistoric archaeology$2bicssc 606 $aPlant ecology$2bicssc 610 $aarcheology 610 $aarcheobotany 610 $aaisen 615 7$aPrehistoric archaeology 615 7$aPlant ecology 700 $aBelmar$b Carolina$4auth$01280643 906 $aBOOK 912 $a9910688320603321 996 $aLos cazadores-recolectores y las plantas en Patagonia$93165895 997 $aUNINA LEADER 03687nam 22006855 450 001 9911022454403321 005 20260223145841.0 010 $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 606 $aInferència$2thub 606 $aAprenentatge automàtic$2thub 606 $aOptimització matemàtica$2thub 608 $aLlibres electrònics$2thub 615 0$aMathematical optimization. 615 0$aStatistics. 615 0$aMachine learning. 615 14$aOptimization. 615 24$aBayesian Inference. 615 24$aMachine Learning. 615 7$aInferència 615 7$aAprenentatge automàtic 615 7$aOptimització matemàtica 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