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A Bayes Decision Rule to Assist Policymakers during a Pandemic
Kang Hua Cao
, Paul Damien
*
, Chi Keung Woo
, Jay Zarnikau
*
Corresponding author for this work
Research output
:
Contribution to journal
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Journal article
›
peer-review
1
Citation (Scopus)
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Keyphrases
Politicians
100%
Bayes Rule
100%
United States
33%
New York
33%
Rule-based
33%
Sensitivity Analysis
33%
Texas
33%
Lockdown
33%
Econometric Models
33%
Net Benefit
33%
COVID-19 Data
33%
Florida
33%
Bayesian Econometrics
33%
Markov Chain Monte Carlo Algorithm
33%
COVID-19 Outcomes
33%
Earth and Planetary Sciences
United States
100%
United States of America
100%
Markov Chain Monte Carlo
100%
Texas
100%
New York
100%
Florida
100%
Economics, Econometrics and Finance
Bayesian
100%
Econometric Model
100%
Markov Chain Monte Carlo
100%
United States
100%
Social Sciences
COVID-19
100%
United States of America
50%
Lockdown
50%
Texas
50%
Markov Chain Monte Carlo
50%
Bayesian
50%
Psychology
COVID-19
100%
Markov Chain Monte Carlo
50%