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  Evaluating Bayesian Radiocarbon-dated Event Count (REC) models for the study of long-term human and environmental processes

Carleton, W. C. (2020). Evaluating Bayesian Radiocarbon-dated Event Count (REC) models for the study of long-term human and environmental processes. Journal of Quaternary Science, 36(1): 3256, pp. 110-123. doi:10.1002/jqs.3256.

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 Creators:
Carleton, W. Christopher1, Author              
Affiliations:
1Max Planck Research Group Extreme Events, Max Planck Institute for the Science of Human History, Max Planck Society, ou_3262629              

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Free keywords: archaeology, Bayesian regression, palaeoclimatology, radiocarbon dating, radiocarbon-dated event count (REC) model
 Abstract: Chronological uncertainty complicates attempts to use radiocarbon dates as proxies for processes such as human population growth/decline, forest fires and marine ingression. Established approaches involve turning databases of radiocarbon-date densities into single summary proxies that cannot fully account for chronological uncertainty. Here, I use simulated data to explore an alternative Bayesian approach that instead models the data as what they are, namely radiocarbon-dated event counts. The approach involves assessing possible event-count sequences by sampling radiocarbon date densities and then applying a Markov Chain Monte Carlo method to estimate the parameters of an appropriate count-based regression model. The regressions based on individual sampled sequences were placed in a multilevel framework, which allowed for the estimation of hyperparameters that account for chronological uncertainty in individual event times. Two processes were used to produce simulated data. One represented a simple monotonic change in event-counts and the other was based on a real palaeoclimate proxy record. In both cases, the method produced estimates that had the correct sign and were consistently biased towards zero. These results indicate that the approach is widely applicable and could form the basis of a new class of quantitative models for use in exploring long-term human and environmental processes.

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Language(s): eng - English
 Dates: 2020-10-20
 Publication Status: Published online
 Pages: 14
 Publishing info: -
 Table of Contents: Introduction
Materials and methods
- Simulated data
- Regression experiments
- Radiocarbon‐dated event‐count (REC) model
Results
- Stage 1: monotonic process
- Stage 2: real palaeoclimate process
Discussion
Conclusions
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/jqs.3256
Other: shh2762
 Degree: -

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Title: Journal of Quaternary Science
  Abbreviation : J. Quaternary Sci
Source Genre: Journal
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Publ. Info: Chichester : John Wiley & Sons, Ltd.
Pages: - Volume / Issue: 36 (1) Sequence Number: 3256 Start / End Page: 110 - 123 Identifier: ISSN: 0267-8179
CoNE: https://pure.mpg.de/cone/journals/resource/954925500137