• Skip to main content
  • Skip to after header navigation
  • Skip to site footer
ERN: Emerging Researchers National Conference in STEM

ERN: Emerging Researchers National Conference in STEM

  • About
    • About AAAS
    • About the NSF
    • About the Conference
    • Partners/Supporters
    • Project Team
  • Conference
  • Abstracts
    • Undergraduate Abstract Locator
    • Graduate Abstract Locator
    • Abstract Submission Process
    • Presentation Schedules
    • Abstract Submission Guidelines
    • Presentation Guidelines
  • Travel Awards
  • Resources
    • Award Winners
    • Code of Conduct-AAAS Meetings
    • Code of Conduct-ERN Conference
    • Conference Agenda
    • Conference Materials
    • Conference Program Books
    • ERN Photo Galleries
    • Events | Opportunities
    • Exhibitor Info
    • HBCU-UP/CREST PI/PD Meeting
    • In the News
    • NSF Harassment Policy
    • Plenary Session Videos
    • Professional Development
    • Science Careers Handbook
    • Additional Resources
    • Archives
  • Engage
    • Webinars
    • ERN 10-Year Anniversary Videos
    • Plenary Session Videos
  • Contact Us
  • Login

Comparing Programming Languages for Distribution Computation

Undergraduate #235
Discipline: Computer Sciences and Information Management
Subcategory: Computer Science & Information Systems

Levie C. McGee - Philander Smith College


In this research, the performance of a variety of programming languages when considering the task of probability distribution computation is investigated. Computing exact probability distributions is a computational intensive task. Various programming languages such as Matlab and Fortran have been utilized in an attempt to minimize the computational burden of executing this task for certain scenarios; however, to date, there does not appear to be any formal comparison of programming languages for the completion of this task. Researchers tend to rely on the recommendation of other researchers, which is often biased towards the language with which they have the most familiarity or comfort. Recently, methodology has been established to permit the computation of the exact distribution of the multiple window scan statistic for multi-state, higher-order Markovian sequences. This methodology is given in the literature with a complete distribution of the algorithm and pseudo code, which makes it easily accessible for the kind of investigation we are interested in. In the spirit of Aruoba et. al., we conduct a comparison of speed and accessibility to novice programmers for this algorithm, using Fortran and C++. We share our findings about the conditions under which each program performs best. In future work, we will consider a further comparison to computing the distribution using the R program.

Funder Acknowledgement(s): Arkansas Louis Stokes Alliance for Minority Participation Program funded by NSF award number HRD-1304121 and the Arkansas Science and Technology Authority funded by NSF award number HRD-1304121 and the Arkansas Science and Technology Authority.

Faculty Advisor: Deidra A. Coleman, dcoleman@philander.edu

Role: I computed wrote a program in C++ to compare to the Fortran program. In addition, I developed subroutines in both C++ and Fortran to ease development and produced tables and graphs of my findings.

Sidebar

Abstract Locators

  • Undergraduate Abstract Locator
  • Graduate Abstract Locator

This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

AAAS

1200 New York Ave, NW
Washington,DC 20005
202-326-6400
Contact Us
About Us

  • LinkedIn
  • Facebook
  • Instagram
  • Twitter
  • YouTube

The World’s Largest General Scientific Society

Useful Links

  • Membership
  • Careers at AAAS
  • Privacy Policy
  • Terms of Use

Focus Areas

  • Science Education
  • Science Diplomacy
  • Public Engagement
  • Careers in STEM

Focus Areas

  • Shaping Science Policy
  • Advocacy for Evidence
  • R&D Budget Analysis
  • Human Rights, Ethics & Law

© 2023 American Association for the Advancement of Science