Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package

Software Impacts 4 (1):100016 (2020)
Download Edit this record How to cite View on PhilPapers
The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan languageā€™s no-U-turn (NUTS) sampler. The package combines the ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing.
PhilPapers/Archive ID
Upload history
Archival date: 2020-05-05
View other versions
Added to PP index

Total views
211 ( #35,708 of 72,542 )

Recent downloads (6 months)
40 ( #21,085 of 72,542 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.