Showcasing Innovative Computing Research
Our KSU Supercomputing Spotlight Series helps showcase groundbreaking research and its applications in our communities. We have looked into Bayesian Statistics, a powerful framework for statistical inference that integrates prior knowledge into the analysis and agendamelding and its impact during the COVID-19 pandemic. Stay up to date and find out how to get involved with upcoming Spotlight Series!
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2024 KSU Supercomputing Spotlight Series
Bayesian Statistics and its Application in Social Science and Business Research
Bayesian Statistics is a useful framework for statistical inference that has found widespread application in various fields, including social science and business research. Unlike traditional frequentist statistics, Bayesian statistics incorporates prior knowledge or beliefs into the analysis, allowing for more flexible and nuanced modeling. This presentation introduces Bayesian Statistics and its application in social science and business research to a non-technical audience.
Bayesian Statistics and its Application in Social Science and Business Research
Bayesian Statistics is a useful framework for statistical inference that has found widespread application in various fields, including social science and business research. Unlike traditional frequentist statistics, Bayesian statistics incorporates prior knowledge or beliefs into the analysis, allowing for more flexible and nuanced modeling. This presentation introduces Bayesian Statistics and its application in social science and business research.
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2023 KSU Supercomputing Spotlight Series
Agendamelding and COVID: Research and Impact
March 17, 12 PM - 1 PM on Teams. This presentation provides a brief history of the agenda-setting agendamelding lines of research and explores the relationship between the individual, social media, and traditional media agendas during the COVID-19 pandemic.
Agendamelding and COVID: Impementation and Demonstration
March 24 link, 12 PM - 1 PM on Teams. This presentation recaps the steps that the authors took to collect 224 million tweets about the pandemic and how the HPC was utilized to parse the data, analyze and classify the text of the tweets, and performs analyses and time series to test their hypothesis.