@Article{ref_263525,
author = {Buckberry, J. L. and Chamberlain, A. T.},
title = {{Age estimation from the auricular surface of the ilium: a revised method}},
journal = {{American Journal of Physical Anthropology}},
volume = {119},
number = {3},
doi = {10.1002/ajpa.10130},
pages = {231–239},
year = {2002},
abstract = {A revised method for estimating adult age at death using the auricular surface of the ilium has been developed. It is based on the existing auricular surface aging method of Lovejoy et al. ([1985] Am. J. Phys. Anthropol. 68:15-28), but the revised technique is easier to apply, and has low levels of inter- and intraobserver error. The new method records age-related stages for different features of the auricular surface, which are then combined to provide a composite score from which an estimate of age at death is obtained. Blind tests of the method were carried out on a known-age skeletal collection from Christ Church, Spitalfields, London. These tests showed that the dispersion of age at death for a given morphological stage was large, particularly after the first decade of adult life. Statistical analysis showed that the age-related changes in auricular surface are not significantly different for males and females. The scores from the revised method have a slightly higher correlation with age than do the Suchey-Brooks pubic symphysis stages. Considering the higher survival rates of the auricular surface compared with the pubic symphysis, this method promises to be useful for biological anthropology and forensic science.},
location = {Department of Archaeology and Prehistory, University of Sheffield, Sheffield S1 4ET, UK. J.Buckberry@Sheffield.ac.uk},
keywords = {}}

@Book{ref_29376,
author = {Cowie, Robert and Bekvalac, Jelena and Kausmally, Tania},
title = {{Late 17th- to 19th-century burial and earlier occupation at All Saints, Chelsea Old Church, Royal Borough of Kensington and Chelsea}},
series = {MoLAS archaeology studies series},
volume = {18},
pages = {xii, 69},
editor = {},
publisher = {Museum of London Archaeology Service},
address = {London},
year = {2008},
abstract = {},
keywords = {Chelsea Old Church (London, England); Excavations (Archaeology) England London; Human remains (Archaeology) England London; Cemeteries England London; Paleopathology England London; Chelsea (London, England) Antiquities; Chelsea Old Church London, England}}

@Article{derwood_2016,
    title = {{runjags}: An {R} Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for {MCMC} Models in {JAGS}},
    author = {Matthew J. Denwood},
    journal = {Journal of Statistical Software},
    year = {2016},
    volume = {71},
    number = {9},
    pages = {1--25},
    doi = {10.18637/jss.v071.i09}
  }

  @article{FUCHS20261,
title = {Age-related disease or disease-related age? Perspectives for paleopathological research},
journal = {International Journal of Paleopathology},
volume = {53},
pages = {1-11},
year = {2026},
issn = {1879-9817},
doi = {https://doi.org/10.1016/j.ijpp.2026.02.003},
url = {https://www.sciencedirect.com/science/article/pii/S1879981726000100},
author = {Katharina Fuchs and Jo Appleby and Marie Louise Schjellerup Jørkov and George R. Milner and Niels Lynnerup and Katherine D. {van Schaik} and Julia Gresky and Fabian Crespo and Molly Zuckerman and Kathryn E. Marklein},
keywords = {Aging, Age at death, Pathophysiology, Skeletal marker formation, Paleoepidemiology, Paleodemography}}

@Article{gabry_et_al_2019,
    title = {Visualization in Bayesian workflow},
    author = {Jonah Gabry and Daniel Simpson and Aki Vehtari and Michael Betancourt and Andrew Gelman},
    year = {2019},
    journal = {Journal of the Royal Statistical Society A},
    volume = {182},
    issue = {2},
    pages = {389-402},
    doi = {10.1111/rssa.12378},
  }

@Article{gneiting_et_al_2007,
author = {Gneiting, Tilmann and Balabdaoui, Fadoua and Raftery, Adrian E.},
title = {Probabilistic forecasts, calibration and sharpness},
journal = {{Journal of the Royal Statistical Society Series B: Statistical Methodology}},
volume = {69},
number = {2},
pages = {243--268},
year = {2007}}

@Article{ref_110209,
author = {Gompertz, Benjamin},
title = {On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies},
journal = {{Philosophical Transactions of the Royal Society of London}},
volume = {115},
number = {},
doi = {10.1098/rstl.1825.0026},
pages = {513--583},
year = {1825},
abstract = {},
location = {},
keywords = {}}

@Book{wickham_2016,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {https://ggplot2.tidyverse.org},
  }

@Manual{kay_2024,
    title = {{tidybayes}: Tidy Data and Geoms for {Bayesian} Models},
    author = {Matthew Kay},
    year = {2024},
    note = {R package version 3.0.7},
    url = {http://mjskay.github.io/tidybayes/},
    doi = {10.5281/zenodo.1308151},
  }

  @Article{kay_2024b,
    author = {Matthew Kay},
    title = {{ggdist}: Visualizations of Distributions and Uncertainty in the Grammar of Graphics},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    year = {2024},
    volume = {30},
    number = {1},
    pages = {414--424},
    doi = {10.1109/TVCG.2023.3327195},
  }

@Manual{kassambara_2025,
    title = {ggpubr: 'ggplot2' Based Publication Ready Plots},
    author = {Alboukadel Kassambara},
    year = {2025},
    note = {R package version 0.6.2},
    url = {https://CRAN.R-project.org/package=ggpubr},
    doi = {10.32614/CRAN.package.ggpubr},
  }

@Article{konigsberg_et_al_2008,
author = {Konigsberg, Lyle W. and Herrmann, Nicholas P. and Wescott, Daniel J. and Kimmerle, Erin H.},
title = {Estimation and Evidence in Forensic Anthropology: Age-at-Death},
journal = {Journal of Forensic Sciences},
volume = {53},
number = {3},
pages = {541--557},
year = {2008},
keywords = {forensic science; pubic symphysis; probit analysis; likelihood ratio},}


@Book{ref_77051,
author = {Kruschke, John K.},
title = {{Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan}},
series = {},
volume = {},
pages = {XII, 759},
editor = {},
publisher = {Academic Press},
address = {Amsterdam},
year = {2015},}

@Article{ref_54568,
author = {Lewandowski, Daniel and Kurowicka, Dorota and Joe, Harry},
title = {{Generating random correlation matrices based on vines and extended onion method}},
journal = {{Journal of Multivariate Analysis}},
volume = {100},
number = {9},
doi = {10.1016/j.jmva.2009.04.008},
pages = {1989–2001},
year = {2009},
abstract = {},
location = {},
keywords = {}}

@Article{ref_65832,
author = {Lovejoy, C. Owen and Meindl, Richard S. and Pryzbeck, Thomas R. and Mensforth, Robert P.},
title = {{Chronological metamorphosis of the auricular surface of the ilium: A new method for the determination of adult skeletal age at death}},
journal = {{American Journal of Physical Anthropology}},
volume = {68},
number = {1},
doi = {10.1002/ajpa.1330680103},
pages = {15–28},
year = {1985},
abstract = {Abstract A new method for the determination of adult skeletal age at death based upon chronological changes in the auricular surface of the ilium is presented. Formal stages have been constructed following extensive tests and refinements in observations made of such changes. Two completely ?blind? tests were conducted to assess the accuracy and bias of the new method. Results show that the system is equally accurate to pubic symphyseal aging (although somewhat more difficult to apply), and also carries the advantages of a higher preservation rate for the auricular surface in archaeological populations and continued age-related change beyond the fifth decade.},
location = {},
keywords = {Auricular; Ilium; Forensic; Age-determination; Demography; Sacroiliac}}

@Incollection{ref_21132,
author = {Lovejoy, C. Owen and Meindl, Richard S. and Tague, Robert G. and Latimer, Bruce},
title = {{The comparative senescent biology of the hominoid pelvis and its implications for the use of age-at-death indicators in the human skeleton}},
booktitle = {{Integrating archaeological demography: Multidisciplinary approaches to prehistoric population}},
series = {Center for Archaeological Investigations Occasional paper},
volume = {24},
editor = {},
publisher = {Center for Archaeological Investigations},
address = {Carbondale, Ill.},
pages = {43-63},
year = {1997},
abstract = {},
keywords = {}}

@Article{missov_et_al_2015,
author = {Missov, Trifon I. and Lenart, Adam and Nemeth, Laszlo and Canudas-Romo, Vladimir and Vaupel, James W.},
title = {The Gompertz force of mortality in terms of the modal age at death},
journal = {Demographic Research},
volume = {32},
pages = {1031--1048},
year = {2015},
abstract = {The Gompertz force of mortality (hazard function) is usually expressed in terms of a, the initial level of mortality, and b, the rate at which mortality increases with age. <b>Objective</b>: We express the Gompertz force of mortality in terms of b and the old-age modal age at death M, and present similar relationships for other widely-used mortality models. Our objective is to explain the advantages of using the parameterization in terms of M. <b>Methods</b>: Using relationships among life table functions at the modal age at death, we express the Gompertz force of mortality as a function of the old-age mode. We estimate the correlation between the estimators of old (a and b) and new (M and b) parameters from simulated data. <b>Results</b>: When the Gompertz parameters are statistically estimated from simulated data, the correlation between estimated values of b and M is much less than the correlation between estimated values of a and b. For the populations in the Human Mortality Database, there is a negative association between a and b and a positive association between M and b. <b>Conclusions</b>: Using M, the old-age mode, instead of a, the level of mortality at the starting age, has two major advantages. First, statistical estimation is facilitated by the lower correlation between the estimators of model parameters. Second, estimated values of M are more easily comprehended and interpreted than estimated values of a.}, }

@Article{ref_128246,
author = {Moser, Daria and Pichler, Sandra L. and Santos, Ana Luisa and Klimscha, Florian and Fuchs, Katharina},
title = {{The uncertain death. Estimating mortality structure by random sampling}},
journal = {{International Journal of Osteoarchaeology}},
volume = {35},
number = {1},
doi = {10.1002/oa.3363},
pages = {e3363},
year = {2025},
abstract = {Abstract Imprecise data produced by commonly applied osteological age-at-death estimations profoundly affect all research on age-dependent mortality in past societies. With uncertain death estimation (UCD), we propose a novel approach to estimating the mortality structure from imprecise data and present a corresponding R package for simple application. Through repeated random sampling of imprecise age-at-death ranges, UCD estimates the mortality structure of a given skeletal sample. We demonstrate the applicability of UCD in a proof-of-principle study on two samples with known age at death (Bass-Mercyhurst Collection and Coimbra Identified Skeletal Collection). Two case studies of German Neolithic skeletal material illustrate UCD’s applicability to archeological samples with dissimilar states of preservation. To comparatively quantify the accuracy of UCD, maximum likelihood estimations, Kaplan?Meier survival estimations, and age-category mortality profiles were calculated for all four study samples. UCD outperforms similar existing procedures while incorporating the uncertainty inherent in osteological data. The proof-of-principle study produced significantly more accurate mortality profiles from UCD than from maximum likelihood estimation and Kaplan?Meier survival estimation. Both archeological case studies indicate UCD’s ability to provide meaningful new insight into age-dependent mortality in past societies. UCD allows for comparative studies into age-dependent mortality in past societies without requiring a large sample of precise age-at-death estimations. UCD provides an opportunity for fast and simple analysis of mortality structures on a large dataset without neglecting the information contained in the raw data, thereby facilitating a critical study of patterns in age-dependent mortality on a large scale.},
location = {},
keywords = {age at death; mortality; Neolithic; paleodemography; random sampling}}

@Article{ref_26972,
author = {Müller-Scheeßel, Nils and Rinne, Christoph and Fuchs, Katharina},
title = {{Adult mortality in the metropolis of London 1100–1850: A Bayesian view based on osteological data}},
journal = {{American Journal of Biological Anthropology}},
volume = {185},
number = {3},
doi = {10.1002/ajpa.25025},
pages = {e25025},
year = {2024},
abstract = {Abstract Objectives The present paper highlights the potential of osteological data for answering demographic questions through the development and application of a Bayesian approach incorporating age estimations of adults. Materials and methods The metropolis of London is a rewarding study region for such an approach due to the large number of cemeteries, with thousands of individuals, spanning a period from 1100?AD to the mid-19th century, that have been excavated and published in the past two decades. Furthermore, London has a rich tradition of early written records on mortality that can be used for comparison purposes. In order to gain comparable results from the different source categories, we use a Bayesian framework in which the Gompertz distribution features centrally. Results Our intensive simulations show that the Bayesian approach performs better than maximum likelihood estimation or ordinary least squares in terms of recovering the original age distribution parameters. They also show how strongly population growth affects mortality patterns. The osteological data suggests that during medieval times and the Early Modern period, the modal age-at-death of the general population of London remained more or less constant and hovered around 30?years, and that only monks showed a higher modal age, of about 45?years. However, from the 17th century onwards, life expectancy of adults increased markedly. Discussion Our comparison of written sources and osteological data demonstrates their general comparability and highlights the much higher mortality among adults in London than in the rest of England and Wales. We conclude that Bayesian modeling has great potential for recovering true mortality patterns from osteological data.},
location = {},
keywords = {adult mortality; Bayesian modeling; Gompertz distribution; London; palaeodemography; simulation}}


@Article{ref_299013,
author = {Müller-Scheeßel, Nils and Rinne, Christoph and Fuchs, Katharina},
title = {{A Fully Bayesian Approach to Adult Skeletal Age Estimation: Multivariate Latent Trait Modeling with Markov Chain Monte Carlo Sampling}},
journal = {{American Journal of Biological Anthropology}},
volume = {190},
number = {2},
doi = {10.1002/ajpa.70289},
pages = {e70289},
year = {2026},
abstract = {},
location = {},
keywords = {adult mortality; Bayesian modeling; Gompertz distribution; London; palaeodemography; simulation}}


@Incollection{plummer_2003,
author = {Plummer, Martyn},
title = {JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling},
booktitle = {Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, 20-22 March 2003},
editor = {Hornik, Kurt and Leisch, Friedrich and Zeileis, Achim},
publisher = {Technische Universität Wien},
address = {Wien},
pages = {1-10},
year = {2003} }

@Article{plummer_et_al_2006,
    title = {CODA: Convergence Diagnosis and Output Analysis for MCMC},
    author = {Martyn Plummer and Nicky Best and Kate Cowles and Karen Vines},
    journal = {R News},
    year = {2006},
    volume = {6},
    number = {1},
    pages = {7--11},
    pdf = {https://journal.r-project.org/articles/RN-2006-002/RN-2006-002.pdf},
}

@Article{91048,
author = {Sasaki, Tomohiko and Kondo, Osamu},
title = {An informative prior probability distribution of the gompertz parameters for bayesian approaches in paleodemography},
journal = {American Journal of Physical Anthropology},
volume = {159},
number = {3},
pages = {523–533},
year = {2016},
abstract = {ABSTRACT Objectives In paleodemography, the Bayesian approach has been suggested to provide an effective means by which mortality profiles of past populations can be adequately estimated, and thus avoid problems of ?age-mimicry? inherent in conventional approaches. In this study, we propose an application of the Gompertz model using an ?informative? prior probability distribution by revising a recent example of the Bayesian approach based on an ?uninformative? distribution. Methods Life-table data of 134 human populations including those of contemporary hunter-gatherers were used to determine the Gompertz parameters of each population. In each population, we used both raw life-table data and the Gompertz parameters to calculate some demographic values such as the mean life-span, to confirm representativeness of the model. Then, the correlation between the two Gompertz parameters (the Strehler?Mildvan correlation) was re-established. We incorporated the correlation into the Bayesian approach as an ?informative? prior probability distribution, and tested its effectiveness using simulated data. Results Our analyses showed that the mean life-span (≥ age 15) and the proportion of living persons aging over 45 were well-reproduced by the Gompertz model. The simulation showed that using the correlation as an informative prior provides a narrower estimation range in the Bayesian approach than does the uninformative prior. Conclusions The Gompertz model can be assumed to accurately estimate the mean life-span and/or the proportion of old people in a population. We suggest that the Strehler?Mildvan correlation can be used as a useful constraint in demographic reconstructions of past human populations. Am J Phys Anthropol 159:523?533, 2016. ? 2015 Wiley Periodicals, Inc.},
keywords = {Strehler–Mildvan correlation; Gompertz hazard model; life table; age mimicry},}

@Article{strehler_mildvan_1960,
author = {Strehler, Bernard L. and Mildvan, Albert S.},
title = {General Theory of Mortality and Aging},
journal = {Science},
volume = {132},
number = {3418},
pages = {14--21},
year = {1960}, }

@Article{stull_et_al_2022,
author = {Stull, Kyra E. and Chu, Elaine Y. and Corron, Louise K. and Price, Michael H.},
title = {Subadult Age Estimation Using the Mixed Cumulative Probit and a Contemporary United States Population},
journal = {Forensic Sciences},
volume = {2},
number = {4},
pages = {741--779},
year = {2022},
abstract = {The mixed cumulative probit (MCP), a new, flexible algorithm that accommodates a variety of mean and shape parameters in univariate models and conditional dependence/independence in multivariate models, was used to develop subadult age estimation models. Sixty-two variables were collected on computed tomography (CT) images of 1317 individuals (537 females and 780 males) aged between birth and 21 years from the United States sample in the Subadult Virtual Anthropology Database (SVAD). Long bone measurements (n = 18), stages of epiphyseal fusion and ossification (n = 28), and stages of dental development of permanent teeth (n = 16) were used in univariate, multivariate, and mixed models and compared using test mean log posterior (TMNLP), root mean squared error (RMSE), and percent accuracy on an independent test sample. Out of the six possible parameter combinations, all combinations were accounted for at least once in the data and conditionally dependent models outperformed the conditionally independent models. Overall, multivariate models exhibited smaller TMNLP and RMSE, and an overall greater stability in the age estimations compared to univariate models across all ages and independent of indicator type. Pre-optimized subadult age estimation models are freely available for immediate application through MCP-S-Age, a graphical user interface.}, }

@Article{valpine_et_al_2017,
    title = {Programming with models: writing statistical algorithms for general model structures with {NIMBLE}},
    journal = {Journal of Computational and Graphical Statistics},
    volume = {26},
    issue = {2},
    pages = {403-413},
    year = {2017},
    author = {Perry {de Valpine} and Daniel Turek and Christopher Paciorek and Cliff Anderson-Bergman and Duncan {Temple Lang} and Ras Bodik},
    doi = {10.1080/10618600.2016.1172487},
  }

@Manual{wilke_2025,
    title = {ggridges: Ridgeline Plots in 'ggplot2'},
    author = {Claus O. Wilke},
    year = {2025},
    note = {R package version 0.5.7},
    url = {https://CRAN.R-project.org/package=ggridges},
    doi = {10.32614/CRAN.package.ggridges},
  }

@Incollection{wood_et_al_2002,
author = {Wood, James W. and Holman, Darryl J. and O'Connor, Kathleen A. and Ferrell, Rebecca J.},
title = {Mortality models for paleodemography},
booktitle = {Paleodemography: Age Distributions from Skeletal Samples},
editor = {Hoppa, Robert D. and Vaupel, James W.},
publisher = {Cambridge University Press},
address = {Cambridge},
pages = {129-168},
year = {2002},
keywords = {Paläoanthropologie; Paläodemographie},}
