A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data
Authors: Vos, D., Stafford, R., Jenkins, E.L. and Garrard, A.
Journal: PLoS ONE
Volume: 16
Issue: 3 March 2021
eISSN: 1932-6203
DOI: 10.1371/journal.pone.0248261
Abstract:The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of different sources of information in order to cross validate findings and combat issues of ambiguity and equifinality. However, the application of a multiproxy approach often generates incompatible data, and might therefore still provide ambiguous results. This paper explores the potential of a simple digital framework to increase the explanatory power of multiproxy data by enabling the incorporation of incompatible, ambiguous datasets in a single model. In order to achieve this, Bayesian confirmation was used in combination with decision trees. The results of phytolith and geochemical analyses carried out on soil samples from ephemeral sites in Jordan are used here as a case study. The combination of the two datasets as part of a single model enabled us to refine the initial interpretation of the use of space at the archaeological sites by providing an alternative identification for certain activity areas. The potential applications of this model are much broader, as it can also help researchers in other domains reach an integrated interpretation of analysis results by combining different datasets.
https://eprints.bournemouth.ac.uk/36899/
Source: Scopus
A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data.
Authors: Vos, D., Stafford, R., Jenkins, E.L. and Garrard, A.
Journal: PLoS One
Volume: 16
Issue: 3
Pages: e0248261
eISSN: 1932-6203
DOI: 10.1371/journal.pone.0248261
Abstract:The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of different sources of information in order to cross validate findings and combat issues of ambiguity and equifinality. However, the application of a multiproxy approach often generates incompatible data, and might therefore still provide ambiguous results. This paper explores the potential of a simple digital framework to increase the explanatory power of multiproxy data by enabling the incorporation of incompatible, ambiguous datasets in a single model. In order to achieve this, Bayesian confirmation was used in combination with decision trees. The results of phytolith and geochemical analyses carried out on soil samples from ephemeral sites in Jordan are used here as a case study. The combination of the two datasets as part of a single model enabled us to refine the initial interpretation of the use of space at the archaeological sites by providing an alternative identification for certain activity areas. The potential applications of this model are much broader, as it can also help researchers in other domains reach an integrated interpretation of analysis results by combining different datasets.
https://eprints.bournemouth.ac.uk/36899/
Source: PubMed
A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data
Authors: Vos, D., Stafford, R., Jenkins, E.L. and Garrard, A.
Journal: PLOS ONE
Volume: 16
Issue: 3
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0248261
https://eprints.bournemouth.ac.uk/36899/
Source: Web of Science (Lite)
A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data.
Authors: Vos, D., Stafford, R., Jenkins, E.L. and Garrard, A.
Journal: PloS one
Volume: 16
Issue: 3
Pages: e0248261
eISSN: 1932-6203
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0248261
Abstract:The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of different sources of information in order to cross validate findings and combat issues of ambiguity and equifinality. However, the application of a multiproxy approach often generates incompatible data, and might therefore still provide ambiguous results. This paper explores the potential of a simple digital framework to increase the explanatory power of multiproxy data by enabling the incorporation of incompatible, ambiguous datasets in a single model. In order to achieve this, Bayesian confirmation was used in combination with decision trees. The results of phytolith and geochemical analyses carried out on soil samples from ephemeral sites in Jordan are used here as a case study. The combination of the two datasets as part of a single model enabled us to refine the initial interpretation of the use of space at the archaeological sites by providing an alternative identification for certain activity areas. The potential applications of this model are much broader, as it can also help researchers in other domains reach an integrated interpretation of analysis results by combining different datasets.
https://eprints.bournemouth.ac.uk/36899/
Source: Europe PubMed Central
A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data
Authors: Vos, D., Stafford, R., Jenkins, E.L. and Garrard, A.
Journal: PLoS One
Volume: 16
Issue: 3
ISSN: 1932-6203
Abstract:The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of different sources of information in order to cross validate findings and combat issues of ambiguity and equifinality. However, the application of a multiproxy approach often generates incompatible data, and might therefore still provide ambiguous results. This paper explores the potential of a simple digital framework to increase the explanatory power of multiproxy data by enabling the incorporation of incompatible, ambiguous datasets in a single model. In order to achieve this, Bayesian confirmation was used in combination with decision trees. The results of phytolith and geochemical analyses carried out on soil samples from ephemeral sites in Jordan are used here as a case study. The combination of the two datasets as part of a single model enabled us to refine the initial interpretation of the use of space at the archaeological sites by providing an alternative identification for certain activity areas. The potential applications of this model are much broader, as it can also help researchers in other domains reach an integrated interpretation of analysis results by combining different datasets.
https://eprints.bournemouth.ac.uk/36899/
Source: BURO EPrints