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A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data

Vos, D; Stafford, R; Jenkins, EL; Garrard, A; (2021) A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data. PLoS One , 16 (3) , Article e0248261. 10.1371/journal.pone.0248261. Green open access

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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.

Type: Article
Title: A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0248261
Publisher version: http://dx.doi.org/10.1371/journal.pone.0248261
Language: English
Additional information: © 2021 Vos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology > Institute of Archaeology Gordon Square
URI: https://discovery.ucl.ac.uk/id/eprint/10126164
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