La Viet-Phuong - Academia.edu (original) (raw)

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

Software Impacts, 2020

The exponential growth of social data both in volume and complexity has increasingly exposed many... more The exponential growth of social data both in volume and complexity has increasingly exposed many ofthe shortcomings of the conventional frequentist approach to statistics. The scientific community has calledfor careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics,still faces considerable barriers toward a more widespread application. ThebayesvlR package is an openprogram, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn(NUTS) sampler. The package combines the ability to construct Bayesian network models using directed acyclicgraphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of theggplot2package. As a result, it can improve the user experience and intuitive understanding when constructingand analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the packagefor Big Data analytics and cognitive computing.

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

Software Impacts, 2020

The exponential growth of social data both in volume and complexity has increasingly exposed many... more The exponential growth of social data both in volume and complexity has increasingly exposed many ofthe shortcomings of the conventional frequentist approach to statistics. The scientific community has calledfor careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics,still faces considerable barriers toward a more widespread application. ThebayesvlR package is an openprogram, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn(NUTS) sampler. The package combines the ability to construct Bayesian network models using directed acyclicgraphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of theggplot2package. As a result, it can improve the user experience and intuitive understanding when constructingand analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the packagefor Big Data analytics and cognitive computing.

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