Determining the Sex of Infanticide Victims from the Late Roman Era through Ancient DNA Analysis

Faerman, Marina; Bar‐Gal, Gila Kahila; Filon, Dvora; Greenblatt, Charles L.; Stager, Lawrence E.; Oppenheim, Ariella; Smith, Patricia

doi:10.1006/jasc.1997.0231

Cited by 90 publications

(51 citation statements)

References 21 publications

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“…In the section on sequential use of Bayes Theorem, we mentioned that Mays and Faerman essentially stopped their (frequentist) analysis at twelve males and five females which included the four individuals from Waldron et al (1999). Though the data from Faerman et al (1998) were certainly available, Mays and Faerman mentioned the male bias in that data, and did not include this additional data that would have brought the number of total male infant deaths to 26 and female deaths to 10. The apparent reason for their exclusion of this earlier data from any further chisquare calculations was the possibility that the sample used in the Faerman et al study was derived from a brothel, based on archaeological context (the remains were recovered around a bath house) and a male bias among the remains (which they interpreted as female babies having economic value in the presumed context).…”

Section: Discussionmentioning

confidence: 99%

“…This brought the total to 12 males and 5 females. Although they did not include aDNA data from the Late Roman Era site of Ashkelon (Faerman et al, 1998), this data (14 males and 5 females) could potentially be combined with the 12 males and 5 females to yield 26 total males and 10 females. Using Jeffreys' prior, the posterior density would then be a Be 26:5; 10:5 ð Þdistribution.…”

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

“…In the above paragraph, we approached the analysis as if we started with Jeffreys' prior and then used the data on 36 infants (Faerman et al, 1998;Waldron et al, 1999;Mays and Faerman, 2001) to find the posterior density of Be 26:5; 10:5 ð Þ . But we can also use yesterday's posterior as tomorrow's prior, so we could treat the data in the order that it arrived in the literature.…”

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

“…But we can also use yesterday's posterior as tomorrow's prior, so we could treat the data in the order that it arrived in the literature. Starting with Jeffreys' prior and the Faerman et al (1998) data gives a posterior density of Be 14:5; 5:5 ð Þ which we could use as a prior for Waldron et al's (1999) data to obtain a posterior of Be 17:5; 6:5 ð Þ . This in turn could be used as a prior for Mays and Faerman's (2001) data to arrive at the posterior of Be 26:5; 10:5 ð Þ .…”

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

“…A Bayes factor of 1.671 classifies as a grade 1, or "not worth more than a bare mention," on Jeffreys' scale. If, on the other hand, we consider all of the data from Faerman et al (1998), Waldron et al (1999), and Mays and Faerman (2001) under a Jeffreys prior and compare that to the probability of getting the same data if h was exactly equal to 0.513, then we have BB 26; 36; 0:5; 0:5 ð Þin the numerator and the binomial probability for getting 26 males out of 36 individuals (with h50:513) in the denominator. This gives a Bayes factor of about 3.5, which classifies on Jeffreys' scale as a grade 2, or "evidence against the null is substantial.…”

Section: Bayes Factormentioning

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: Discussionmentioning

confidence: 99%

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

Section: Bayes Factormentioning

confidence: 99%

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Bayes in biological anthropology

Konigsberg

Frankenberg

2013

American J Phys Anthropol

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Sex assessment on the basis of long bone circumference

Safont

Malgosa

Subirà

2000

Am. J. Phys. Anthropol.

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Discriminant functions have long been used to classify individuals into groups according to the dimensions of their bones. Although lengths, widths, and diameters have been extensively used, the circumferences have not been adequately validated. In this work, the importance that the circumferences of long bones can have in assigning the sex of ancient human remains is demonstrated. The functions produced by using just one circumference achieved accuracies higher than 80%, and circumference at the radial tuberosity of the radius is able to classify 92.8% of skeletons from the Late Roman site of Mas Rimbau/Mas Mallol (Spain). When functions are produced by using more than one circumference, they can achieve the uppermost classification attained in this sample. The functions also showed that the arm circumference functions are more useful than those of the leg, probably because male individuals of the population had greater mechanical stress than did females. The classification percentages, as well as other statistical values for the functions, demonstrated the great ability of long bone circumferences in helping to classify the sex of individuals of other sites of the Mediterranean area besides the ones examined in this study.

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2019

A Companion to Anthropological Genetics

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Determining the Sex of Infanticide Victims from the Late Roman Era through Ancient DNA Analysis

Cited by 90 publications

References 21 publications

Bayes in biological anthropology

Bayes in biological anthropology

Sex assessment on the basis of long bone circumference

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