Determining Provenance from Compositional Data (Elements in Current Archaeological Tools and Techniques) - Softcover
Synopsis
Traditionally, 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. This Element 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). To 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 labeling, a semi-supervised algorithm is applied to obsidian samples.
"synopsis" may belong to another edition of this title.
Search results for Determining Provenance from Compositional Data (Elements...
Stock Image
Determining Provenance from Compositional Data (Paperback)
Published by
Cambridge University Press, Cambridge, 2026
ISBN 10: 1009634178
ISBN 13: 9781009634175
New
Paperback
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Traditionally, 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. This Element 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). To 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 labeling, a semi-supervised algorithm is applied to obsidian samples. Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. This Element reconsiders the use of statistical methods for provenance analysis of archaeological materials using a step-by-step procedure. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009634175
Stock Image
Determining Provenance From Compositional Data
Published by
Cambridge University Press, 2026
ISBN 10: 1009634178
ISBN 13: 9781009634175
New
Paperback
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. In Stock. This item is printed on demand. Seller Inventory # __1009634178
Buy New
£ 17.53
£ 10 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Stock Image
Determining Provenance from Compositional Data (eng)
Published by
Cambridge University Press, 2026
ISBN 10: 1009634178
ISBN 13: 9781009634175
New
Softcover
Seller: Brook Bookstore On Demand, Napoli, NA, Italy
Seller rating 4 out of 5 stars
Condition: new. Seller Inventory # Z9GOA487WY
Stock Image
Determining Provenance From Compositional Data
Published by
Cambridge University Press, 2026
ISBN 10: 1009634178
ISBN 13: 9781009634175
New
Paperback
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. 86 pages. 6.00x0.18x9.00 inches. In Stock. Seller Inventory # x-1009634178
Buy New
£ 25.78
£ 10 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 2 available
Stock Image
Determining Provenance from Compositional Data (Paperback)
Published by
Cambridge University Press, Cambridge, 2026
ISBN 10: 1009634178
ISBN 13: 9781009634175
New
Paperback
Seller: AussieBookSeller, Truganina, VIC, Australia
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Traditionally, 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. This Element 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). To 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 labeling, a semi-supervised algorithm is applied to obsidian samples. Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. This Element reconsiders the use of statistical methods for provenance analysis of archaeological materials using a step-by-step procedure. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781009634175
Seller Image
Determining Provenance from Compositional Data
Published by
Cambridge University Press, 2026
ISBN 10: 1009634178
ISBN 13: 9781009634175
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Traditionally, 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. This Element 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). To 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 labeling, a semi-supervised algorithm is applied to obsidian samples. Seller Inventory # 9781009634175
Seller Image
Determining Provenance from Compositional Data
Published by
Cambridge University Press, 2026
ISBN 10: 1009634178
ISBN 13: 9781009634175
New
Taschenbuch
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Determining Provenance from Compositional Data | Pedro A. López-García (u. a.) | Taschenbuch | Englisch | 2026 | Cambridge University Press | EAN 9781009634175 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 134744751