scholarly journals Bayesian Approaches to the Building of Archaeological Chronologies

2015 ◽  
pp. 272-292 ◽  
Author(s):  
Christopher Ramsey
Keyword(s):  
Bayesian Approaches ◽  
Archaeological Chronologies

scholarly journals Rolling Out Revolution: Using Radiocarbon Dating in Archaeology

Radiocarbon ◽  
2009 ◽  
Vol 51 (1) ◽  
pp. 123-147 ◽  
Author(s):  
Alex Bayliss
Keyword(s):  
Radiocarbon Dating ◽  
Bayesian Approaches ◽  
The World ◽  
Archaeological Chronologies ◽  
Death Knell ◽  
The Revolution

Sixty years ago, the advent of radiocarbon dating rewrote archaeological chronologies around the world. Forty years ago, the advent of calibration signaled the death knell of the diffusionism that had been the mainstay of archaeological thought for a century. Since then, the revolution has continued, as the extent of calibration has been extended ever further back and as the range of material that can be dated has been expanded. Now a new revolution beckons, one that could allow archaeology to engage in historical debate and usher in an entirely new kind of (pre)history. This paper focuses on more than a decade of experience in utilizing Bayesian approaches routinely for the interpretation of14C dates in English archaeology, discussing both the practicalities of implementing these methods and their potential for changing archaeological thinking.

scholarly journals Bayesian graphical models for modern biological applications

2021 ◽  
Author(s):  
Yang Ni ◽  
Veerabhadran Baladandayuthapani ◽  
Marina Vannucci ◽  
Francesco C. Stingo
Keyword(s):  
Graphical Models ◽  
Cancer Genomics ◽  
Graph Structure ◽  
Biological Processes ◽  
Limited Sample ◽  
Large Networks ◽  
Bayesian Approaches ◽  
Complex Sampling ◽  
Complex Dependence ◽  
Bayesian Graphical Models

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

scholarly journals Association between Migraine and the Risk of Stroke: A Bayesian Meta-Analysis

2021 ◽  
Vol 13 (7) ◽  
pp. 3759
Author(s):  
Kim-Ngan Ta-Thi ◽  
Kai-Jen Chuang ◽  
Chyi-Huey Bai
Keyword(s):  
Ischemic Stroke ◽  
Hemorrhagic Stroke ◽  
Stroke Risk ◽  
Meta Analysis ◽  
Credible Interval ◽  
Fixed Effect Model ◽  
Bayesian Approaches ◽  
Effect Model ◽  
Newcastle Ottawa Scale ◽  
Form Quality

There are still inconsistent results about association between migraine and stroke risk in studies. This paper was to review findings on the association between migraine (with or without aura) and stroke risk. We searched articles in the Embase and PubMed up to January 2021. Two independent reviewers extracted basic data from individual studies using a standardized form. Quality of studies was also assessed using the Newcastle–Ottawa Scale. We conducted a meta-analysis, both classical and Bayesian approaches. We identified 17 eligible studies with a sample size more than 2,788,000 participants. In the fixed effect model, the results demonstrated that migraine was positively associated with the risk of total stroke, hemorrhagic stroke, and ischemic stroke. Nevertheless, migraine was associated with only total stroke in the random effects model (risk ratio (RR) 1.31, 95%CI: 1.06–1.62). The probability that migraine increased total stroke risk was 0.978 (RR 1.31; 95% credible interval (CrI): 1.01–1.72). All types of migraine were not associated with ischemic stroke and hemorrhagic stroke. Under three prior distributions, there was no association between migraine and the risk of ischemic stroke or hemorrhagic stroke. Under the non-informative prior and enthusiastic prior, there was a high probability that migraine was associated with total stroke risk.

Phylogeny estimation: traditional and Bayesian approaches

2003 ◽  
Vol 4 (4) ◽  
pp. 275-284 ◽  
Author(s):  
Mark Holder ◽  
Paul O. Lewis
Keyword(s):  
Bayesian Approaches ◽  
Phylogeny Estimation

CAN BAYESIAN METHODS MAKE DATA AND ANALYSES MORE RELEVANT TO DECISION MAKERS?

2001 ◽  
Vol 17 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Steven H. Sheingold
Keyword(s):  
Decision Making ◽  
Bayesian Methods ◽  
Paradigm Shift ◽  
Relevant Information ◽  
Decision Makers ◽  
Evidence Based ◽  
Critical Importance ◽  
Bayesian Approaches ◽  
Bayesian Analyses ◽  
And Performance

Decision making in health care has become increasingly reliant on information technology, evidence-based processes, and performance measurement. It is therefore a time at which it is of critical importance to make data and analyses more relevant to decision makers. Those who support Bayesian approaches contend that their analyses provide more relevant information for decision making than do classical or “frequentist” methods, and that a paradigm shift to the former is long overdue. While formal Bayesian analyses may eventually play an important role in decision making, there are several obstacles to overcome if these methods are to gain acceptance in an environment dominated by frequentist approaches. Supporters of Bayesian statistics must find more accommodating approaches to making their case, especially in finding ways to make these methods more transparent and accessible. Moreover, they must better understand the decision-making environment they hope to influence. This paper discusses these issues and provides some suggestions for overcoming some of these barriers to greater acceptance.

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