Version 1
: Received: 29 December 2019 / Approved: 2 January 2020 / Online: 2 January 2020 (05:19:27 CET)
How to cite:
Frometa-Castillo, T.; Pyakuryal, A.; Wals-Zurita, A.; Mesbahi, A. Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions. Preprints2020, 2020010014. https://doi.org/10.20944/preprints202001.0014.v1
Frometa-Castillo, T.; Pyakuryal, A.; Wals-Zurita, A.; Mesbahi, A. Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions. Preprints 2020, 2020010014. https://doi.org/10.20944/preprints202001.0014.v1
Frometa-Castillo, T.; Pyakuryal, A.; Wals-Zurita, A.; Mesbahi, A. Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions. Preprints2020, 2020010014. https://doi.org/10.20944/preprints202001.0014.v1
APA Style
Frometa-Castillo, T., Pyakuryal, A., Wals-Zurita, A., & Mesbahi, A. (2020). Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions. Preprints. https://doi.org/10.20944/preprints202001.0014.v1
Chicago/Turabian Style
Frometa-Castillo, T., Amadeo Wals-Zurita and Asghar Mesbahi. 2020 "Computational Simulations of Similar Probabilistic Distributions to the Binomial and Poisson Distributions" Preprints. https://doi.org/10.20944/preprints202001.0014.v1
Abstract
This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these legendary distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of the random discrete variable X. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.