Improving Bayesian Statistics Understanding in the Age of Big Data With the Bayesvl R Package

Software Impacts, 4, 100016, 2020

DOI: 10.1016/j.simpa.2020.100016

4 Pages Posted: 4 Jun 2020

See all articles by Quan Hoang Vuong

Quan Hoang Vuong

Université Libre de Bruxelles (ULB) - Solvay Brussels School of Economics and Management; Phenikaa University

Viet-Phuong La

Vuong & Associates

Minh-Hoang Nguyen

Centre for Interdisciplinary Social Research, Phenikaa University

Manh-Toan Ho

Vuong & Associates; Phenikaa University, Center for Interdisciplinary Social Research, Students; Thanh Tay University

Mạnh Tùng Hồ

Ritsumeikan Asia Pacific University

Peter Mantello

affiliation not provided to SSRN

Date Written: 2020

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.

Suggested Citation

Vuong, Quan Hoang and La, Viet-Phuong and Nguyen, Minh-Hoang and Ho, Manh-Toan and Ho, Manh-Toan and Hồ, Mạnh Tùng and Mantello, Peter, Improving Bayesian Statistics Understanding in the Age of Big Data With the Bayesvl R Package (2020). Software Impacts, 4, 100016, 2020, DOI: 10.1016/j.simpa.2020.100016, Available at SSRN: https://ssrn.com/abstract=3476275

Quan Hoang Vuong

Université Libre de Bruxelles (ULB) - Solvay Brussels School of Economics and Management ( email )

ULB CP 145/01
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Belgium
+32-2-6504864 (Phone)
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Phenikaa University ( email )

To Huu road, Yen Nghia
Ha Dong district
Hanoi, Hanoi 100803
Vietnam

HOME PAGE: http://sites.google.com/site/vuongqh2019/

Viet-Phuong La

Vuong & Associates ( email )

3/161 Thinh Quang
Dong Da District
Hanoi, 100000
Vietnam

Minh-Hoang Nguyen

Centre for Interdisciplinary Social Research, Phenikaa University ( email )

Hanoi
Vietnam

Manh-Toan Ho (Contact Author)

Vuong & Associates ( email )

3/161 Thinh Quang
Dong Da District
Hanoi, 100000
Vietnam

Phenikaa University, Center for Interdisciplinary Social Research, Students ( email )

Hanoi
Vietnam

Thanh Tay University ( email )

Yen Nghia Ward, Ha Dong District
Hanoi, 100803
Vietnam

Mạnh Tùng Hồ

Ritsumeikan Asia Pacific University ( email )

1-1 Jumonjibaru
Beppu City, Oita 874-8577
Japan

Peter Mantello

affiliation not provided to SSRN

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