Bayesian Statistical Modeling of Disinfection Byproduct (DBP) Bromine Incorporation in the ICR Database

2010 ◽  
Vol 44 (4) ◽  
pp. 1232-1239 ◽  
Author(s):  
Royce A. Francis ◽  
Jeanne M. VanBriesen ◽  
Mitchell J. Small
Keyword(s):  
Statistical Modeling ◽  
Disinfection Byproduct ◽  
Bayesian Statistical Modeling

scholarly journals Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling

2019 ◽  
Vol 10 ◽  
Author(s):  
Kento Koyama ◽  
Zafiro Aspridou ◽  
Shige Koseki ◽  
Konstantinos Koutsoumanis
Keyword(s):  
Statistical Modeling ◽  
Thermal Inactivation ◽  
Bayesian Statistical Modeling

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.

The Global Diffusion of the #MeToo Movement

2020 ◽  
pp. 1-29
Author(s):  
Myunghee Lee ◽  
Amanda Murdie
Keyword(s):  
Civil Rights ◽  
Statistical Modeling ◽  
Significant Role ◽  
Civil Liberties ◽  
Political Opportunity ◽  
Feminist Activism ◽  
Opportunity Structures ◽  
Country Level ◽  
Political Opportunity Structures ◽  
Bayesian Statistical Modeling

Abstract Why is the #MeToo movement very active in some countries but not in others? What factors encourage the transnational diffusion of digital feminist activism? Although transnational forces are important, we argue that domestic political opportunity structures play a more significant role than transnational influences in the country-level diffusion of #MeToo. We collected 35,211 global tweets and used Bayesian statistical modeling to test the implications of our theory. Our findings support the idea that as a country better protects its citizens’ political and civil rights and civil liberties, individuals in that country are more likely to engage in the #MeToo movement.

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