Bayes' theorem in paleopathological diagnosis

Byers, Steven N.; Roberts, Charlotte A.

doi:10.1002/ajpa.10164

Cited by 20 publications

(7 citation statements)

References 65 publications

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“…Although there are proof-ofconcept examples in several application areas in Buck et al (1996) and others have also published examples (Orton, 2000;Millard, 2002;Millard and Gowland, 2002;Byers and Roberts, 2003;Millard, 2004Millard, , 2005Finke et al, 2008;van Leusen et al, 2009;Fernandes et al, 2014), there is really only one application area where Bayesian methods can be said to be routine: absolute, scientific-dating-based chronology construction. There is one other application area with close connections to archaeology, that of phylogeny (both genetic and linguistic), where use of Bayesian methods is also increasingly routine (Drummond et al, 2004;Edwards et al, 2007;Kitchen et al, 2009;Drummond et al, 2012;Bouckaert et al, 2014).…”

Section: Bayesian Archaeologymentioning

confidence: 99%

On being agoodBayesian

Buck

Meson

2015

World Archaeology

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Bayesianism is fast becoming the dominant paradigm in archaeological chronology construction. This paradigm shift has been brought about in large part by widespread access to tailored computer software which provides users with powerful tools for complex statistical inference with little need to learn about statistical modelling or computer programming. As a result, we run the risk that such software will be reduced to the status of black-boxes. This would be a dangerous position for our community since good, principled, use of Bayesian methods requires mindfulness when selecting the initial model, defining prior information, checking the reliability and sensitivity of the software runs and interpreting the results obtained. In this paper, we provide users with a brief review of the nature of the care required and offer some comments and suggestions to help ensure that our community continues to be respected for its philosophically rigorous scientific approach.

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Section: Bayesian Archaeologymentioning

confidence: 99%

On being agoodBayesian

Buck

Meson

2015

World Archaeology

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“…Bayesian statistics were successfully used as a diagnostic tool in clinical medicine (e.g., Prince, 1996;Starmer and Lee, 1976), and also in several anthropological applications, including stature estimation (Konigsberg et al, 1998;Ross and Konigsberg, 2002), age estimation (Lucy et al, 1996;Schmitt et al, 2002), paleopathological diagnosis (Byers and Roberts, 2003), and most recently, frontal sinus comparisons (Christiansen, 2005). The utility of a Bayesian approach in forensic science, including ballistic (Bunch, 2000) and paint matching (McDermott et al, 1999), drug sampling and analysis (Coulson et al, 2001;Goldmann et al, 2004), and DNA identification (Krawczak and Schmidtke, 1992;Evett and Weir, 1998;Thompson et al, 2003;Foreman et al, 1999), is widely known, as it provides the examiner with the likelihood ratio of a positive identification or match that can be presented in court.…”

Section: Osteological Data Likelihood Ratios and Personal Identificmentioning

confidence: 99%

Statistical basis for positive identification in forensic anthropology

Steadman

Adams

Konigsberg

2006

Am. J. Phys. Anthropol.

107

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Forensic scientists are often expected to present the likelihood of DNA identifications in US courts based on comparative population data, yet forensic anthropologists tend not to quantify the strength of an osteological identification. Because forensic anthropologists are trained first and foremost as physical anthropologists, they emphasize estimation problems at the expense of evidentiary problems, but this approach must be reexamined. In this paper, the statistical bases for presenting osteological and dental evidence are outlined, using a forensic case as a motivating example. A brief overview of Bayesian statistics is provided, and methods to calculate likelihood ratios for five aspects of the biological profile are demonstrated. This paper emphasizes the definition of appropriate reference samples and of the "population at large," and points out the conceptual differences between them. Several databases are introduced for both reference information and to characterize the "population at large," and new data are compiled to calculate the frequency of specific characters, such as age or fractures, within the "population at large." Despite small individual likelihood ratios for age, sex, and stature in the case example, the power of this approach is that, assuming each likelihood ratio is independent, the product rule can be applied. In this particular example, it is over three million times more likely to obtain the observed osteological and dental data if the identification is correct than if the identification is incorrect. This likelihood ratio is a convincing statistic that can support the forensic anthropologist's opinion on personal identity in court.

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“…The purpose here is not to make individual diagnoses. Most often individual diagnoses can-like individual age estimates (Hoppa and Vaupel, 2002)-only be reached by applying Bayes' theorem after the population distribution has been established (see e.g., Byers and Roberts, 2003). Boldsen and Freund (2006) have published a statistical algorithm, and a small Windows based program to facilitate individualbased analyses of leprosy in the past.…”

Section: Osteological Methodsmentioning

confidence: 99%

Leprosy in the early medieval Lauchheim community

Boldsen

2007

American J Phys Anthropol

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Leprosy was a well-recognized and dreaded disease in medieval Europe (5th-15th century AD). It is reported to have reached Germany with the Roman invasion. A much larger fraction than previously assumed appears to have been affected by leprosy in the medieval period. This article estimates the frequency (i.e., the prevalence at death) of leprosy among adult people buried in the Lauchheim early medieval cemetery. Seven different dichotomous osteological lesions indicative of leprosy are analyzed, and it is possible to score at least one of these conditions on 110 adult skeletons (aged 15 or more). The scores were transformed to a statistic--lambda (lambda)--indicating the likelihood that the person to whom the skeleton belonged suffered from leprosy. The analyses indicate that 16% (95% confidence interval: 9-23%) of adult people in Lauchheim died with osteological signs of leprosy. Leprosy was significantly more prevalent among men than women. The lambda statistic indicates that people who died with signs of leprosy did not differ in the distribution of age at death from those who did not have such signs. Some of the leprosy-related lesions had a statistically significant nonrandom dispersal on the cemetery; but there is no clear pattern to this and the significant results could be easily attributed to a type-1 error in the statistical analysis.

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Bayes' theorem in paleopathological diagnosis

Cited by 20 publications

References 65 publications

On being agoodBayesian

On being agoodBayesian

Statistical basis for positive identification in forensic anthropology

Leprosy in the early medieval Lauchheim community

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