Estimating the age structure of a buried adult population: A new statistical approach applied to archaeological digs in France

Séguy, Isabelle; Caussinus, Henri; Courgeau, Daniel; Buchet, Luc

doi:10.1002/ajpa.22187

Cited by 33 publications

(25 citation statements)

References 14 publications

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“…While it is certainly possible to build MCMC models in R, as reflected in recent years in the AJPA (F€ urtbauer et al, 2013;Gillespie et al, 2013;S eguy et al, 2013), it is more typical for researchers to use one of the freely available packages to apply MCMC (Millard and Gowland, 2002;Barik et al, 2008;Matsuda et al, 2010;Babb et al, 2011;Matauschek et al, 2011;Yang et al, 2012;Gilmore, 2013;Muchlinski et al, 2013;Raaum et al, 2013;Zinner et al, 2013). Among the freely available packages are: BATWING, BayesTraits, BEAST, JAGS, MrBayes, OpenBUGS, Stan, STRUC-TURE, and WinBUGS.…”

Section: Using Openbugs For Paleoanthropological Analysismentioning

confidence: 99%

“…Possibly as an over-reaction to the somewhat Bayesian past of age estimation in paleodemography, the "Rostock volume" (Hoppa and Vaupel, 2002) took a decidedly frequentist approach to the field. More recently, a number of applications in paleodemography have taken a more explicitly Bayesian approach (Chamberlain, 2000;Gowland and Chamberlain, 2002;Millard and Gowland, 2002;Bocquet-Appel and Bacro, 2008;Caussinus and Courgeau, 2010;S eguy et al, 2013). Of these, only Caussinus and Courgeau (2010) and S eguy et al (2013) take a fully Bayesian approach by applying MCMC to produce the full posterior distributions for the proportions of individuals in each age class.…”

Section: Bayes In Bioarchaeologymentioning

confidence: 99%

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Bayes in biological anthropology

Konigsberg

Frankenberg

2013

American J Phys Anthropol

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In this article, we both contend and illustrate that biological anthropologists, particularly in the Americas, often think like Bayesians but act like frequentists when it comes to analyzing a wide variety of data. In other words, while our research goals and perspectives are rooted in probabilistic thinking and rest on prior knowledge, we often proceed to use statistical hypothesis tests and confidence interval methods unrelated (or tenuously related) to the research questions of interest. We advocate for applying Bayesian analyses to a number of different bioanthropological questions, especially since many of the programming and computational challenges to doing so have been overcome in the past two decades. To facilitate such applications, this article explains Bayesian principles and concepts, and provides concrete examples of Bayesian computer simulations and statistics that address questions relevant to biological anthropology, focusing particularly on bioarchaeology and forensic anthropology. It also simultaneously reviews the use of Bayesian methods and inference within the discipline to date. This article is intended to act as primer to Bayesian methods and inference in biological anthropology, explaining the relationships of various methods to likelihoods or probabilities and to classical statistical models. Our contention is not that traditional frequentist statistics should be rejected outright, but that there are many situations where biological anthropology is better served by taking a Bayesian approach. To this end it is hoped that the examples provided in this article will assist researchers in choosing from among the broad array of statistical methods currently available. Am J Phys Anthropol 57: 2013. V C 2013 Wiley Periodicals, Inc."The Bayesian approach can have a clarifying effect on one's thinking about evidence." (Koehler and Saks, 1991, p 364) Traditional training in statistical methods for those who go on to become practicing biological anthropologists has focused primarily on classical hypothesis testing. This is apparent in both textbooks geared toward anthropologists in general (Thomas, 1986;Madrigal, 1998;Bernard, 2011) and specialized texts for biological anthropologists (Slice, 2005;D'Aoãut and Vereecke, 2011). While Bayes' Theorem may be mentioned in passing in introductory statistics courses, this is typically restricted to examples of such limited interest that the student has little motivation to recall the theorem, and even less motivation to assume that there may be future value in having learned about Bayes' Theorem. In Bayesian terms, the prior probability that the student will retain Bayes' Theorem is quite low. In contrast, the student and eventual practitioner is likely to learn about confidence intervals, Type I and Type II errors in hypothesis testing, and P-values, and to blithely assume that what they have learned represents the near totality of what is available and useful within modern statistical practice. This represents an unfortunate omission of Ba...

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Section: Using Openbugs For Paleoanthropological Analysismentioning

confidence: 99%

Section: Bayes In Bioarchaeologymentioning

confidence: 99%

Bayes in biological anthropology

Konigsberg

Frankenberg

2013

American J Phys Anthropol

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“…Bayesian approaches have previously been designed and successfully applied in, for example, paleodemography (12,13) and radiocarbon dating in archaeology (14); however, they are not readily applied to data typically collected in anthropological fieldwork on small-scale societies. Our method requires two inputs: first, a single ranking or multiple partial rankings of individuals by age obtained from interviewing members of the population, and, second, an arbitrary a priori age distribution per individual chosen by the researchers familiar with the population, which can be based on accurate measures or on "eyeballing."…”

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confidence: 99%

Accurate age estimation in small-scale societies

Diekmann

Smith

Gerbault

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Precise estimation of age is essential in evolutionary anthropology, especially to infer population age structures and understand the evolution of human life history diversity. However, in small-scale societies, such as hunter-gatherer populations, time is often not referred to in calendar years, and accurate age estimation remains a challenge. We address this issue by proposing a Bayesian approach that accounts for age uncertainty inherent to fieldwork data. We developed a Gibbs sampling Markov chain Monte Carlo algorithm that produces posterior distributions of ages for each individual, based on a ranking order of individuals from youngest to oldest and age ranges for each individual. We first validate our method on 65 Agta foragers from the Philippines with known ages, and show that our method generates age estimations that are superior to previously published regression-based approaches. We then use data on 587 Agta collected during recent fieldwork to demonstrate how multiple partial age ranks coming from multiple camps of hunter-gatherers can be integrated. Finally, we exemplify how the distributions generated by our method can be used to estimate important demographic parameters in small-scale societies: here, age-specific fertility patterns. Our flexible Bayesian approach will be especially useful to improve cross-cultural life history datasets for small-scale societies for which reliable age records are difficult to acquire. A ccurate estimation of the age of individuals is essential in evolutionary anthropology. Major questions in the field require an accurate inference of the timing of life history events, such as age at menarche, age at first reproduction, age at cessation of reproduction, interbirth intervals, and death. Age is also essential when assessing infant growth or developmental trajectories and when estimating age structure properties of a population (e.g., the potential for population growth or decline, recovering signatures of epidemics, assessing vulnerability to ecological perturbations). Humans have important derived life history features, such as shorter interbirth intervals, longer life span, extended postreproductive longevity, and childhood dependence (1). These life history traits vary across species in the slow/fast continuum (2), and they likely vary within humans in response to differences in ecology, such as differential mortality rates (3) and energetics (4). However, due to unreliable age estimations, very few studies have highlighted variability in life history traits in traditional societies (3,5,6). The challenge of estimating ages is particularly problematic for populations where individuals do not relate their age to calendar years, as is the case among many hunter-gatherer and other small-scale societies (7,8). Although longitudinal studies (7, 9) are an ideal approach to address questions about variation in life history traits in smallscale populations, these studies are rare. There is consequently a need for methods to estimate ages based on cross-sectional data fro...

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“…Recently, Bayesian approaches have been developed to circumvent this age mimicry problem (Caussinus and Courgeau, 2010;Konigsberg and Frankenberg, 2013;S eguy et al, 2013). The advantage of the Bayesian approach is that it can be based solely on information that is independent of the reference sample age distribution.…”

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confidence: 99%

“…Only in this way can the posterior probability distribution be obtained as a measure of confidence of the target age distribution of deaths. To date, however, there have been few paleodemographic studies that have applied such a proper Bayesian approach (Caussinus and Courgeau, 2010;Konigsberg and Frankenberg, 2013;S eguy et al, 2013).…”

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confidence: 99%