{"title":"Denisse L. Argote","description":"\u003cp\u003eDenisse L. Argote's works delve into the intricate intersection of history and science, offering insightful perspectives on the origins and movement of historical artefacts. Her analytical approach, as seen in titles like \u003cem\u003eDetermining Provenance from Compositional Data\u003c\/em\u003e, provides readers with a detailed understanding of how scientific methods can illuminate historical narratives.\u003c\/p\u003e\n\n\u003cp\u003eReaders interested in the nuanced exploration of historical evidence through quantitative analysis will find Argote's contributions both rigorous and enlightening. Her books challenge traditional viewpoints by integrating compositional data to uncover deeper truths within historical and military studies.\u003c\/p\u003e","products":[{"product_id":"determining-provenance-from-compositional-data-by-denisse-l-argote-9781009634168","title":"Determining Provenance from Compositional Data","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003eTraditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made.\u003c\/p\u003e\n\n\u003cp\u003e\u003cem\u003eDetermining Provenance from Compositional Data\u003c\/em\u003e reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data. This approach results in better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups, which are common in biplots.\u003c\/p\u003e\n\n\u003cp\u003eTo evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labelling, a semi-supervised algorithm is applied to obsidian samples.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Unknown","offers":[{"title":"Default Title","offer_id":47933953474796,"sku":"9781009634168","price":213.0,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/9781009634168-determining-provenance-from-compositional-data.jpg?v=1783887505"},{"product_id":"determining-provenance-from-compositional-data-by-denisse-l-argote-9781009634175","title":"Determining Provenance from Compositional Data","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003eTraditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made.\u003c\/p\u003e\n\n\u003cp\u003e\u003cem\u003eDetermining Provenance from Compositional Data\u003c\/em\u003e reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data, thus obtaining better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups (common in biplots).\u003c\/p\u003e\n\n\u003cp\u003eTo evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labelling, a semi-supervised algorithm is applied to obsidian samples.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Unknown","offers":[{"title":"Default Title","offer_id":47933953507564,"sku":"9781009634175","price":75.0,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/9781009634175-determining-provenance-from-compositional-data.jpg?v=1783887522"}],"url":"https:\/\/bookhero.pro\/collections\/denisse-l-argote.oembed","provider":"Book Hero","version":"1.0","type":"link"}