scholarly journals Constraining absolute chronologies with the application of Bayesian analysis

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (2) ◽  
pp. 14 ◽  
Author(s):  
Francesco Maspero ◽  
Emanuela Sibilia ◽  
Marco Martini
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Statistics ◽  
Case Studies ◽  
Bayesian Approach ◽  
Common Language ◽  
Multiple Data ◽  
The Common ◽  
The Bayesian Approach

<p class="Abstract"><span lang="EN-US">In this work the application of Bayesian statistics to archaeological problems will be discussed. In particular, three case studies will be analyzed, each presenting complex interpretative scenarios, and the most suitable way to solve them. It will be shown that the Bayesian approach allows to refine a dating when in presence of multiple data, even from different dating techniques. The Bayesian approach is presented as the common language between physicists, archaeologists and statisticians to perform more accurate evaluations on stratigraphies and chronologies.</span></p>

scholarly journals A very simple introduction to Bayesian statistics: From coin flips to insight

2020 ◽  
Author(s):  
Daniel Zuckerman
Keyword(s):  
Biological Sciences ◽  
Bayesian Analysis ◽  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Prior Information ◽  
Model Parameters ◽  
Short Article ◽  
Absolute Minimum ◽  
Wet Lab ◽  
The Bayesian Approach

Bayesian statistical analyses are a growing part of the chemical and biological sciences for several reasons. Most importantly, the Bayesian approach of predicting underlying models based on data corresponds naturally with examination of complex systems, whether using wet-lab or computational means. The Bayesian structure also provides a systematic basis for estimating uncertainty in model parameters and permits incorporation of prior information in a quantitative and consistent way. While easy to state in words, these strengths of Bayesian analysis can be difficult to assimilate for beginners. This short article presents essential Bayesian concepts using very simple examples and the absolute minimum mathematics needed to maintain rigor.

scholarly journals Bayesian probabilities for occurrence of large earthquakes in the seismogenic sources of Japan and Phillipine during the period 1998-2017

2001 ◽  
Vol 34 (4) ◽  
pp. 1619
Author(s):  
T. M. TSAPANOS ◽  
O. CH. GALANIS ◽  
S. D. MAVRIDOU ◽  
M. P. HELMl
Keyword(s):  
Seismic Hazard ◽  
Bayesian Statistics ◽  
Poisson Distribution ◽  
Bayesian Approach ◽  
Seismic Sources ◽  
Time Interval ◽  
Seismogenic Sources ◽  
The One ◽  
Large Earthquakes ◽  
The Bayesian Approach

The Bayesian statistics is adopted in 11 seismic sources of Japan and 14 of Philippine in order to estimate the probabilities of occurrence of large future earthquakes, assuming that earthquakes occurrence follows the Poisson distribution. The Bayesian approach applied represents the probability that a certain cut-off magnitude (or larger) will exceed in a given time interval of 20 years, that is 1998-2017. This cut-off magnitude is chosen the one with M=7.0 or greater. In this case we can consider these obtained probabilities as a seismic hazard presentation. More over curves are produced which present the fluctuation of the seismic hazard between these seismic sources. These graphs of varying probability are useful either for engineering or other practical purposes

A Survey of Bayesian Data Mining

Data Mining ◽  
2011 ◽  
pp. 1-26 ◽  
Author(s):  
Stefan Arnborg
Keyword(s):  
Data Mining ◽  
Bayesian Analysis ◽  
Graphical Models ◽  
Bayesian Approach ◽  
Data Sets ◽  
Media Distribution ◽  
Dirichlet Prior ◽  
Approach Problem ◽  
The Bayesian Approach ◽  
Selection Of

This chapter reviews the fundamentals of inference, and gives a motivation for Bayesian analysis. The method is illustrated with dependency tests in data sets with categorical data variables, and the Dirichlet prior distributions. Principles and problems for deriving causality conclusions are reviewed, and illustrated with Simpson’s paradox. The selection of decomposable and directed graphical models illustrates the Bayesian approach. Bayesian and EM classification is shortly described. The material is illustrated on two cases, one in personalization of media distribution, one in schizophrenia research. These cases are illustrations of how to approach problem types that exist in many other application areas.

Bayesian Statistics and Political Recruitment: A Comment

1976 ◽  
Vol 6 (1) ◽  
pp. 124-125
Author(s):  
Paul Whiteley
Keyword(s):  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Subjective Probability ◽  
Analytical Techniques ◽  
Political Activism ◽  
Background Characteristics ◽  
Political Recruitment ◽  
Political Activist ◽  
The Individual ◽  
The Bayesian Approach

In an important contribution to the improvement of data analytical techniques in political science, Budge and Farlie examine the predictive success of various background characteristics in determining political activism [Ian Budge and Dennis Farlie, ‘Political Recruitment and Dropout’, this Journal, v (1975), 33–68]. The authors use the framework of Bayesian statistics, in which the subjective probability that a given individual will be a political activist is revised in the light of sample information about the background characteristics of activists to give a posterior (i.e. after the information or event) probability that the individual is an activist. Unfortunately, as the authors admit, they do not utilize fully all the components of the Bayesian approach.

Bayesian Procedures for Discriminating Among Hypotheses With Discrete Distributions: Inheritance in the Tetraploid Astilbe biternata

Genetics ◽  
1997 ◽  
Vol 147 (4) ◽  
pp. 1933-1942
Author(s):  
Matthew S Olson
Keyword(s):  
Bayesian Analysis ◽  
Prior Knowledge ◽  
Bayesian Approach ◽  
Discrete Distributions ◽  
Southern Appalachians ◽  
Herbaceous Plant ◽  
Bayesian Procedures ◽  
Disomic Inheritance ◽  
Cytogenetic Evidence ◽  
The Bayesian Approach

Abstract Discrimination between disomic and tetrasomic inheritance aids in determining whether tetraploids originated by allotetraploidy or autotetraploidy, respectively. Past assessments of inheritance in tetraploids have used analyses whereby each inheritance hypothesis is tested independently. I present a Bayesian analysis that is appropriate for discriminating among several inheritance hypotheses and can be used in any case where hypotheses are defined by discrete distributions. The Bayesian approach incorporates prior knowledge of the probability of occurrence of disomic and tetrasomic hypotheses so that the results of the analysis are not biased by the fact that there is a single tetrasomic hypothesis and multiple disomic hypotheses. This analysis is used to interpret data from crosses in the tetraploid Astilbe biternata, a herbaceous plant native to the southern Appalachians. The progeny ratios from all crosses favored the hypothesis of disomic inheritance at both the PGM and slow-PGI loci. These results support earlier cytogenetic evidence for the allotetraploid origin of Astilbe biternata.

Testing for Collinearity using Bayesian Analysis

2021 ◽  
pp. 109634802199084
Author(s):  
A. George Assaf ◽  
Mike Tsionas
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Attractive Alternative ◽  
Variance Inflation Factor ◽  
Regression Parameters ◽  
Kolmogorov Smirnov ◽  
Inflation Factor ◽  
The Difference ◽  
The Bayesian Approach ◽  
Smirnov Test

Testing for collinearity continues to be a controversial issue in the literature. Multicollinearity detection criteria, such as the variance inflation factor, often fail to detect the true extent of multicollinearity. In this article, we propose utilizing the Bayesian approach as an attractive alternative. Under the Bayesian approach, we recommend comparing the marginal posterior of regression parameters under two different priors. If the difference in the posterior under these two priors is pronounced, one can surmise that collinearity is harmful. The Kolmogorov–Smirnov test can also be used as further evidence to confirm whether the posterior difference is significant.

Bayesian Statistics in Sociology: Past, Present, and Future

2019 ◽  
Vol 45 (1) ◽  
pp. 47-68 ◽  
Author(s):  
Scott M. Lynch ◽  
Bryce Bartlett
Keyword(s):  
Social Science ◽  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Bayes Theorem ◽  
Widespread Application ◽  
Software Packages ◽  
The Future ◽  
Direct Use ◽  
The Bayesian Approach

Although Bayes’ theorem has been around for more than 250 years, widespread application of the Bayesian approach only began in statistics in 1990. By 2000, Bayesian statistics had made considerable headway into social science, but even now its direct use is rare in articles in top sociology journals, perhaps because of a lack of knowledge about the topic. In this review, we provide an overview of the key ideas and terminology of Bayesian statistics, and we discuss articles in the top journals that have used or developed Bayesian methods over the last decade. In this process, we elucidate some of the advantages of the Bayesian approach. We highlight that many sociologists are, in fact, using Bayesian methods, even if they do not realize it, because techniques deployed by popular software packages often involve Bayesian logic and/or computation. Finally, we conclude by briefly discussing the future of Bayesian statistics in sociology.

Bayesian statistics

2010 ◽  
pp. 477-497
Author(s):  
Janet L. Peacock ◽  
Philip J. Peacock
Keyword(s):  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Posterior Distributions ◽  
Bayesian Analyses ◽  
Prior Distributions ◽  
Unknown Quantity ◽  
Frequentist Approach ◽  
Pros And Cons ◽  
The Bayesian Approach

Bayesian statistics 478 How Bayesian methods work 480 Prior distributions 482 Likelihood; posterior distributions 484 Summarizing and presenting results 486 Using Bayesian analyses in medicine 488 Software for Bayesian statistics 492 Reading Bayesian analyses in papers 494 Bayesian methods: a summary 496 In this chapter we describe the Bayesian approach to statistical analysis in contrast to the frequentist approach. We describe how Bayesian methods work including a description of prior and posterior distributions. We outline the role and choice of prior distributions and how they are combined with the data collected to provide an updated estimate of the unknown quantity being studied. We include examples of the use of Bayesian methods in medicine, and discuss the pros and cons of the Bayesian approach compared with the frequentist approach Finally, we give guidance on how to read and interpret Bayesian analyses in the medical literature....

scholarly journals The promise of Bayesian analysis for prominence seismology

2013 ◽  
Vol 8 (S300) ◽  
pp. 393-394 ◽  
Author(s):  
Iñigo Arregui ◽  
Andrés Asensio Ramos ◽  
Antonio J. Díaz
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Model Comparison ◽  
Physical Models ◽  
Physical Parameters ◽  
Solar Prominence ◽  
Bayesian Techniques ◽  
The Bayesian Approach

AbstractWe propose and use Bayesian techniques for the determination of physical parameters in solar prominence plasmas, combining observational and theoretical properties of waves and oscillations. The Bayesian approach also enables to perform model comparison to assess how plausible alternative physical models/mechanisms are in view of data.

scholarly journals Advantage of Bayesian approach to geotechnical designing

2009 ◽  
Vol 41 (2) ◽  
pp. 83-93
Author(s):  
Kazimierz Garbulewski ◽  
Stanisław Jabłonowski ◽  
Simon Rabarijoely
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Geotechnical Engineering ◽  
Soil Parameters ◽  
Geotechnical Parameters ◽  
The Bayesian Approach

Advantage of Bayesian approach to geotechnical designing The paper addresses the possibility of the Bayesian approach's application to geotechnical engineering. First the principal information on the Bayesian analysis has been presented and its applications to estimate the soil parameters based on the CPT/DMT tests at SGGW Campus in Warsaw afterwards. The CPT/DMT tests had been carried out in order to recognize the geotechnical conditions in the foundations of design campus buildings. The data from two layers of glacial boulder clays have been analysed. The results demonstrate that the Bayesian approach is a useful tool in evaluation of ground properties and estimation of the geotechnical parameters in specified circumstances.

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