Abstract
Abstract 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 language’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.
Highlights
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 lack of intuitive and open programs for Bayesian statistics is unfortunate for the Big Data analytics movement in two senses
As Big Data analytics is increasingly influencing our decisions in business, entertainment, and politics [7,8,9], the more people participate in this movement, the better
Summary
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. As Bayesian statistics, in general, and Bayesian network modeling, in particular, are highly computational methods, it is hard to create a software program for beginners of statistics and machine learning as well as researchers who are used to the frequentist approach.
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