Bradshaw and Bayes: Towards a Timetable for the Neolithic

2007 ◽  
Vol 17 (S1) ◽  
pp. 1-28 ◽  
Author(s):  
Alex Bayliss ◽  
Christopher Bronk Ramsey ◽  
Johannes van der Plicht ◽  
Alasdair Whittle
Keyword(s):  
Visual Inspection ◽  
Probability Distributions ◽  
Prior Beliefs ◽  
Radiocarbon Dates ◽  
Statistical Framework ◽  
Worked Example ◽  
Informative Prior ◽  
Routine Basis

The importance of chronology is reasserted as a means to achieving history and a sense of temporality. A range of current methods for estimating the dates and durations of archaeological processes and events are considered, including visual inspection of graphs and tables of calibrated dates and the summing of the probability distributions of calibrated dates. These approaches are found wanting. The Bayesian statistical framework is introduced, and a worked example presents simulated radiocarbon dates as a demonstration of the explicit, quantified, probabilistic estimates now possible on a routine basis. Using this example, the reliability of the chronologies presented for the five long barrows considered in this series of papers is explored. It is essential that the ‘informative’ prior beliefs in a chronological model are correct. If they are not, the dating suggested by the model will be incorrect. In contrast, the ‘uninformative’ prior beliefs have to be grossly incorrect before the outputs of the model are importantly wrong. It is also vital that the radiocarbon ages included in a model are accurate, and that their errors are correctly estimated. If they are not, the dating suggested by a model may also be importantly wrong. Strenuous effort and rigorous attention to archaeological and scientific detail are inescapable if reliable chronologies are to be built. The dates presented in the following papers are based on models. ‘All models are wrong, some models are useful’ (Box 1979, 202). We hope readers will find them useful, and will employ ‘worry selectivity’ to determine whether and how each model may be importantly wrong. The questions demand the timetable, and our prehistories deserve both.

scholarly journals A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

2018 ◽  
Author(s):  
Seth W. Egger ◽  
Mehrdad Jazayeri
Keyword(s):  
Probability Distributions ◽  
Nonlinear Function ◽  
Alternative View ◽  
Time Interval ◽  
Prior Beliefs ◽  
Optimal Behavior ◽  
Multiple Measurements ◽  
The Brain ◽  
Full Probability ◽  
Sequential Measurements

AbstractBayesian models of behavior have advanced the idea that humans combine prior beliefs and sensory observations to minimize uncertainty. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent and manipulate probability distributions. An alternative view is that brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property makes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that inference strategies humans deploy may deviate from Bayes-optimal integration when the computational demands are high.

scholarly journals A New Approach to the Chronology of Caves 268/272/275 in the Dunhuang Mogao Grottoes: Combining Radiocarbon Dates and Archaeological Information within a Bayesian Statistical Framework

Radiocarbon ◽  
2018 ◽  
Vol 60 (2) ◽  
pp. 667-679
Author(s):  
Qinglin Guo ◽  
Richard A Staff ◽  
Chun Lu ◽  
Cheng Liu ◽  
Michael Dee ◽  
...  
Keyword(s):  
Statistical Modeling ◽  
Ongoing Debate ◽  
Central Plains ◽  
Radiocarbon Dates ◽  
New Approach ◽  
Statistical Framework ◽  
Mogao Grottoes ◽  
Early Buddhism ◽  
The Subject ◽  
Bayesian Statistical Modeling

AbstractThe construction chronology of three of the earliest Dunhuang Mogao Grottoes (Caves 268, 272, and 275) has been the subject of ongoing debate for over half a century. This chronology is a crucial topic in terms of further understanding of the establishment of the Dunhuang Mogao Grottoes, early Buddhism in the Gansu corridor, and its relationship with Buddhism developed in the Central Plains. Building upon archaeological, art historical and radiocarbon (14C) dating studies, we integrate new 14C data with these previously published findings utilizing Bayesian statistical modeling to improve the chronological resolution of this issue. Thus, we determine that all three of these caves were constructed around AD 410–440, suggesting coeval rather than sequential construction.

Anomaly Criteria for General Visual Inspection of Subsea Flexible Pipes

2020 ◽  
Author(s):  
Damir Tadjiev
Keyword(s):  
Visual Inspection ◽  
Service Failure ◽  
Lessons Learned ◽  
Inspection Method ◽  
Deep Waters ◽  
Flexible Pipes ◽  
Increased Risk ◽  
Competent Person ◽  
Routine Basis ◽  
Areas Of Concern

Abstract For flexible pipes in subsea applications, General Visual Inspection (GVI) by Remotely Operated Vehicles (ROV) remains the most common inspection method that is used on a routine basis. It enables verification of pipe configuration or layout and also helps to identify any areas of concern indicative of an increased risk of in-service failure. The success of ROV GVI chiefly relies on the anomaly criteria used, these help inspectors to identify any areas of concern, which can then be assessed by a competent person to ensure any threat to the integrity of an inspected component is identified and addressed. Currently there are no commonly accepted anomaly criteria for ROV GVI of flexible pipes. As a result there is no consistent approach between different operators and experience shows that the inspection approach and anomaly criteria are often adopted from what has traditionally been used for rigid pipes. Since flexible pipes have different design and associated failure threats and mechanisms to rigid pipe, use of this approach may result in under or over inspection of flexible pipes. This paper presents a set of anomaly criteria for ROV GVI of flexible pipes. The criteria were developed using the experience and lessons learned from a population of approximately 350 flexible pipes from two different manufactures operating in deep waters of the UKCS for over a period of 20 years. The criteria cover dynamic flexible risers and associated ancillary equipment, seabed flexible flowlines and jumpers. The applicability of the proposed anomaly criteria to other systems, the benefits of having commonly accepted anomaly criteria, the anomaly detection capability of ROV GVI and the reporting of anomalies are also discussed.

scholarly journals TIMING THE EMERGENCE AND DEVELOPMENT OF ARABLE FARMING IN SOUTHEASTERN NORWAY BY USING SUMMED PROBABILITY DISTRIBUTION OF RADIOCARBON DATES AND A BAYESIAN AGE MODEL

Radiocarbon ◽  
2021 ◽  
pp. 1-22
Author(s):  
Steinar Solheim
Keyword(s):  
Iron Age ◽  
Bayesian Modeling ◽  
Probability Distributions ◽  
Comprehensive Analysis ◽  
Early Iron Age ◽  
Late Neolithic ◽  
Radiocarbon Dates ◽  
Arable Farming ◽  
Age Model

ABSTRACT The paper explores the emergence and development of arable farming in southeastern Norway by compiling and analyzing directly dated cereals from archaeological contexts. By using summed probability distributions of radiocarbon dates and Bayesian modeling, the paper presents the first comprehensive analysis of the directly dated evidence for farming in the region. The models provide a more precise temporal resolution to the development than hitherto presented. The results demonstrate that the introduction of arable farming to southeastern Norway was a long-term development including several steps. Three different stages are pointed out as important in the process of establishing arable farming: the Early and Middle Neolithic, the Late Neolithic, and the Early Iron Age.

Regional variation in the terminal Pleistocene and early Holocene radiocarbon record of eastern North America

2013 ◽  
Vol 79 (2) ◽  
pp. 175-188 ◽  
Author(s):  
D. Shane Miller ◽  
Joseph A.M. Gingerich
Keyword(s):  
North America ◽  
Regional Variation ◽  
Probability Distributions ◽  
Younger Dryas ◽  
Eastern North America ◽  
Northeastern United States ◽  
Radiocarbon Dates ◽  
Terminal Pleistocene ◽  
The Relationship ◽  
Data Result

AbstractIn this paper we use radiocarbon dates to evaluate the signature of the Younger Dryas Chronozone (YDC) in eastern North America. Using an approach that examines radiocarbon dates by region, we argue that the northeastern United States shows a better overall representation of radiocarbon dates when compared to the Mid-Atlantic and Southeast. These data result in a peak in summed probability distributions during the YDC, which is often interpreted as evidence of population growth. Further examination of these distributions, however, illustrates that differential standard deviations, varying sample size, and the effect of taphonomic and research biases likely overwhelm any demographic signatures in our study sample. Consequently, the frequency of radiocarbon dates by itself is insufficient for understanding the relationship between climate, culture and demography in eastern North America.

Climate Sensitivity Distributions Dependence on the Possibility that Models Share Biases

2010 ◽  
Vol 23 (16) ◽  
pp. 4395-4415 ◽  
Author(s):  
Derek M. Lemoine
Keyword(s):  
Temperature Change ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Probability Distributions ◽  
Equilibrium Temperature ◽  
Hierarchical Bayes ◽  
Lapse Rate ◽  
Posterior Distributions ◽  
Prior Beliefs ◽  
Carbon Dioxide Concentrations

Abstract Uncertainty about biases common across models and about unknown and unmodeled feedbacks is important for the tails of temperature change distributions and thus for climate risk assessments. This paper develops a hierarchical Bayes framework that explicitly represents these and other sources of uncertainty. It then uses models’ estimates of albedo, carbon cycle, cloud, and water vapor–lapse rate feedbacks to generate posterior probability distributions for feedback strength and equilibrium temperature change. The posterior distributions are especially sensitive to prior beliefs about models’ shared structural biases: nonzero probability of shared bias moves some probability mass toward lower values for climate sensitivity even as it thickens the distribution’s positive tail. Obtaining additional models of these feedbacks would not constrain the posterior distributions as much as narrowing prior beliefs about shared biases or, potentially, obtaining feedback estimates having biases uncorrelated with those impacting climate models. Carbon dioxide concentrations may need to fall below current levels to maintain only a 10% chance of exceeding official 2°C limits on global average temperature change.

scholarly journals Bayesian Processing of Data on Bursts of Pressure Vessels

2021 ◽  
Vol 50 (4) ◽  
pp. 607-626
Author(s):  
Egidijus Rytas Vaidogas
Keyword(s):  
Epistemic Uncertainty ◽  
Probability Distributions ◽  
Pressure Vessels ◽  
Post Mortem ◽  
Bayesian Approaches ◽  
Prior Distributions ◽  
Informative Prior ◽  
Individual Fragment ◽  
Explosion Accidents ◽  
Bayesian Prior

Two alternative Bayesian approaches are proposed for the prediction of fragmentation of pressure vessels triggered off by accidental explosions (bursts) of these containment structures. It is shown how to carry out this prediction with post-mortem data on fragment numbers counted after past explosion accidents. Results of the prediction are estimates of probabilities of individual fragment numbers. These estimates are expressed by means of Bayesian prior or posterior distributions. It is demonstrated how to elicit the prior distributions from relatively scarce post-mortem data on vessel fragmentations. Specifically, it is suggested to develop priors with two Bayesian models known as compound Poisson-gamma and multinomial-Dirichlet probability distributions. The available data is used to specify non-informative prior for Poisson parameter that is subsequently transformed into priors of individual fragment number probabilities. Alternatively, the data is applied to a specification of Dirichlet concentration parameters. The latter priors directly express epistemic uncertainty in the fragment number probabilities. Example calculations presented in the study demonstrate that the suggested non-informative prior distributions are responsive to updates with scarce data on vessel explosions. It is shown that priors specified with Poisson-gamma and multinomial-Dirichlet models differ tangibly; however, this difference decreases with increasing amount of new data. For the sake of brevity and concreteness, the study was limited to fire induced vessel bursts known as boiling liquid expanding vapour explosions (BLEVEs).

scholarly journals Formal Tests for Resistance-Resilience in Archaeological Time Series

2021 ◽  
Vol 9 ◽  
Author(s):  
Philip Riris ◽  
Jonas Gregorio de Souza
Keyword(s):  
Atlantic Forest ◽  
Probability Distributions ◽  
Significance Test ◽  
Scientific Data ◽  
Policy And Practice ◽  
Cross Sectional ◽  
Radiocarbon Dates ◽  
Hydroclimatic Variability ◽  
Post Hoc

The study of resilience is a common pathway for scientific data to inform policy and practice towards impending climate change. Consequently, understanding the mechanisms and features that contribute towards building resilience is a key goal of much research on coupled socio-environmental systems. In parallel, archaeology has developed the ambition to contribute to this agenda through its unique focus on cultural dynamics that occur over the very long term. This paper argues that archaeological studies of resilience are limited in scope and potential impact by incomplete operational definitions of resilience, itself a multifaceted and contested concept. This lack of interdisciplinary engagement fundamentally limits archaeology’s ability to contribute meaningfully to understanding factors behind the emergence and maintenance of long-term societal resilience, a topic of significant interest that the field is in theory ideally positioned to address. Here, we introduce resilience metrics drawn from ecology and develop case studies to illustrate their potential utility for archaeological studies. We achieve this by extending methods for formally measuring resistance, the capacity of a system to absorb disturbances; and resilience, its capacity to recover from disturbances, with a novel significance test for palaeodemographic data. Building on statistical permutation and post-hoc tests available in the rcarbon package in the R statistical environment, we apply our adapted resilience-resistance framework to summed probability distributions of calibrated radiocarbon dates drawn from the Atlantic Forest of eastern Brazil. We deploy these methods to investigate cross-sectional trends across three recognised biogeographical zones of the Atlantic Forest domain, against the backdrop of prehistoric phases of heightened hydroclimatic variability. Our analysis uncovers novel centennial-scale spatial structure in the resilience of palaeodemographic growth rates. In addition to the case-specific findings, we suggest that adapting formal metrics can help archaeology create impact and engagement beyond relatively narrow disciplinary concerns. To this end, we supply code and data to replicate our palaeodemographic analyses to enable their use and adaptation to other archaeological problems.

scholarly journals A Preliminary Temporal Analysis of the East Texas Archaic

2013 ◽  
Author(s):  
Robert Z. Selden
Keyword(s):  
Probability Distributions ◽  
Archaeological Site ◽  
Temporal Analysis ◽  
Archaic Period ◽  
Temporal Position ◽  
East Texas ◽  
Radiocarbon Dates ◽  
Combination Process ◽  
Early Archaic ◽  
Middle Archaic

This article presents preliminary findings of a temporal analysis of the East Texas Archaic based upon the examination of radiocarbon 14C dates from sites that have deposits that date to the period. All assays employed in this effort were collected from research and cultural resource management reports and publications, synthesized, then recalibrated in version 4.1.7 of OxCal using IntCal09. The date combination process is used herein to refine site-specific summed probability distributions, illustrating— for the first time—the temporal position of each dated archaeological site with an assay that falls within the Archaic. Seventy-three radiocarbon dates from 34 sites serve as the foundation for this analysis of the East Texas Archaic period (ca. 8000-500 B.C.) (Table 1). All dates used in this analysis come directly from the East Texas Radiocarbon Database (ETRD). Within the sample, there are 19 sites with a single radiocarbon sample that dates to the Archaic, eight sites with two dated samples, one site with three dated samples, three sites with four dated samples, one site with five dated samples, and one site with 14 dated samples. Of the 73 14C dates from the ETRD used in this analysis, one dates to the Early Archaic period (ca. 8000-5000 B.C.), eight date to the Middle Archaic period (ca. 5000-3000 B.C.), and the remaining 64 date to the Late Archaic period (ca. 3000-500 B.C.) (temporal divisions follow Perttula and Young).

scholarly journals Collapse and continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya Rapid Climate Change event

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244871
Author(s):  
Dan Lawrence ◽  
Alessio Palmisano ◽  
Michelle W. de Gruchy
Keyword(s):  
Climate Change ◽  
Probability Distributions ◽  
Climate Forcing ◽  
Crop Failure ◽  
Large Spatial Scale ◽  
Radiocarbon Dates ◽  
Rapid Climate Change ◽  
Increased Risk ◽  
Fertile Crescent ◽  
Societal Changes

The rise and fall of ancient societies have been attributed to rapid climate change events. One of the most discussed of these is the 4.2kya event, a period of increased aridity and cooling posited as the cause of societal changes across the globe, including the collapse of the Akkadian Empire in Mesopotamia. Studies seeking to correlate social and climatic changes around the 4.2kya event have tended to focus either on highly localized analyses of specific sites or surveys or more synthetic overviews at pan-continental scales, and temporally on the event and its aftermath. Here we take an empirical approach at a large spatial scale to investigate trends in population and settlement organization across the entirety of Northern Fertile Crescent (Northern Mesopotamia and the Northern Levant) from 6,000 to 3,000 cal BP. We use Summed Probability Distributions of radiocarbon dates and data from eighteen archaeological surveys as proxies for population, and a dataset of all settlements over ten hectares in size as a proxy for the degree of urbanization. The goal is to examine the spatial and temporal impact of the 4.2kya event and to contextualize it within longer term patterns of settlement. We find that negative trends are visible during the event horizon in all three proxies. However, these occur against a long-term trend of increased population and urbanization supported through unsustainable overshoot and the exploitation of a drier zone with increased risk of crop failure. We argue that the 4.2kya event occurred during a period of unprecedented urban and rural growth which may have been unsustainable even without an exogenous climate forcing.

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