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A Note on the Bayesian Modeling of the Stratigraphic Chronology of Canímar Abajo, Cuba

Published online by Cambridge University Press:  22 March 2018

Anne Philippe*
Affiliation:
Laboratoire de mathématiques Jean Leray & Université de Nantes 2, rue de la Houssinière 44000 Nantes, France
Marie-Anne Vibet
Affiliation:
Laboratoire de mathématiques Jean Leray & Université de Nantes 2, rue de la Houssinière 44000 Nantes, France
*
*Corresponding author. Email: anne.philippe@univ-nantes.fr.

Abstract

We implement a Bayesian statistical analysis of the chronology of Canímar Abajo in Cuba in order to estimate two episodes of burial activity and the period of time corresponding to the hiatus between them. We show that by using simple Bayesian modeling, conclusions can easily be reached by the analysis of the marginal posterior distribution of each parameter of the model. However, we also suggest and describe new statistical tools that exploit the joint posterior distribution of collections of dates. These new tools give complementary information regarding the chronology of human activity.

Type
Technical Note
Copyright
© 2018 by the Arizona Board of Regents on behalf of the University of Arizona 

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References

REFERENCES

Bronk Ramsey, C. 2009. Bayesian analysis of radiocarbon dates. Radiocarbon 51(1):337360.Google Scholar
Bronk Ramsey, C. 2016. OxCal 4.2 program.Google Scholar
Chinique de Armas, Y, Buhay, W, Rodríguez Suárez, R, Bestel, S, Smith, D, Mowat, S, Roksandic, M. 2015. Starch analysis and isotopic evidence of consumption of cultigens among fisher–gatherers in Cuba: the archaeological site of Canímar abajo, matanzas. Journal of Archaeological Science 58:121132.Google Scholar
Druffel, EM. 1982. Banded corals: changes in oceanic carbon-14 during the little ice age. Science 218(4567):1319.Google Scholar
Guérin, G, Antoine, P, Schmidt, E, Goval, E, Hérisson, D, Jamet, G, Reyss, J-L, Shao, Q, Philippe, A, Vibet, M-A, Bahain, J-J. 2017. Chronology of the Upper Pleistocene loess sequence of Havrincourt (France) and associated Palaeolithic occupations: a Bayesian approach from pedostratigraphy, OSL, radiocarbon, TL and ESR/U-series data. Quaternary Geochronology 42:1530.Google Scholar
Philippe, A, Vibet, M. 2017. Analysis of archaeological phases using the CRAN package “archaeophases”. Preprint, hal-01347895.Google Scholar
Philippe, A, Vibet, M, Dye, TS. 2017. ArchaeoPhases: Post-Processing of the Markov Chain Simulated by ‘ChronoModel’, ‘Oxcal’ or ‘BCal’. R package version 1.2.Google Scholar
Plummer, M, Best, N, Cowles, K, Vines, K. 2006. Coda: convergence diagnosis and output analysis for mcmc. R News 6(1):711.Google Scholar
Reimer, PJ, Bard, E, Bayliss, A, Beck, JW, Blackwell, PG, Bronk Ramsey, C, Buck, C, Cheng, H, Edwards, RL, Friedrich, M, Grootes, PM, Guilderson, TP, Haflidason, H, Hajdas, I, Hatté, C, Heaton, TJ, Hoffmann, DL, Hogg, AG, Hughen, KA, Kaiser, KF, Kromer, B, Manning, SW, Niu, M, Reimer, RW, Richards, DA, Scott, EM, Southon, JR, Staff, RA, Turney, CSM, van der Plicht, J. 2013. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55(4):18691887.Google Scholar
Roksandic, M, Mark Buhay, W, Chinique de Armas, Y, Rodríguez Suárez, R, Peros, MC, Roksandic, I, Mowat, S, Viera, LM, Arredondo, C, Martínez Fuentes, A, Gray Smith, D. 2015. Radiocarbon and stratigraphic chronology of Canímar Abajo, Matanzas, Cuba. Radiocarbon 57(5):755763.CrossRefGoogle Scholar
Stuiver, M, Reimer, PJ. 1993. Extended 14C data base and revised CALIB 3.0 14C age calibration program. Radiocarbon 35(1):215230.Google Scholar