Twister Generator of Poisson Random Numbers with the Use of Cumulative Frequency Technology
Authors: Deon A.F., Dmitriev D.D., Menyaev Yu.A. | Published: 20.03.2020 |
Published in issue: #1(130)/2020 | |
DOI: 10.18698/0236-3933-2020-1-101-123 | |
Category: Informatics, Computer Engineering and Control | Chapter: Mathematical Support and Software for Computers, Computer Complexes and Networks | |
Keywords: pseudorandom number generator, stochastic sequences, Poisson distribution, twister generator |
The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This paper offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer
References
[1] Feller W. An introduction to probability theory and its applications. Vol. 2. WSE Press, 2008.
[2] Gnedenko B. Theory of probability. CRC Press, 1998.
[3] Zhang H., Li B. Characterizations of discrete compound Poisson distribution. Commun. Stat. --- Theory Methods, 2016, vol. 45, iss. 22, pp. 6789--6802. DOI: https://doi.org/10.1080/03610926.2014.901375
[4] Guerriero V. Power low distribution: method of multi-scale inferential statistics. JMMF, 2012, vol. 1, no. 1, pp. 21--28.
[5] Arkani M., Khalafi H., Vosoughi N. A flexible multichannel digital random pulse generator based on FPGA. WJNST, 2013, vol. 3, no. 4, pp. 109--116. DOI: https://doi.org/10.4236/wjnst.2013.34019
[6] Rasoanaivo A.N., Horowitz W.A. Medium-induced radiation beyond the Poisson approximation. J. Phys.: Conf. Ser., 2017, vol. 878, art. 012029. DOI: https://doi.org/10.1088/1742-6596/878/1/012029
[7] Veiga A., Spinelli E. A pulse generator with poisson-exponential distribution for emulation of radioactive decay events. Proc. 7th LASCAS, 2016, pp. 31--34. DOI: https://doi.org/10.1109/LASCAS.2016.7451002
[8] Kirkpatrick J.M., Young B.M. Poisson statistical methods for the analysis of low-count gamma spectra. IEEE Trans. Nucl. Sci., 2009, vol. 56, iss. 3, pp. 1278--1282. DOI: https://doi.org/10.1109/TNS.2009.2020516
[9] Marsaglia G., Tsang W.W., Wang J. Fast generation of discrete random variables. J. Stat. Softw., 2004, vol. 11, iss. 3, pp. 1--11. DOI: https://doi.org/10.18637/jss.v011.i03
[10] Kumari S., Valarmathi M., Prince S. Generation of pseudorandom binary sequence using shot noise for optical encryption. Proc. ICCSP, 2016, pp. 0119--0122. DOI: https://doi.org/10.1109/ICCSP.2016.7754537
[11] Hosamo M. A study of the source traffic generator using Poisson distribution for ABR service. Model. Simul. Eng., 2012, vol. 2012, art. 408395. DOI: https://doi.org/10.1155/2012/408395
[12] Zhang H., Liu Y., Li B. Notes on discrete compound Poisson model with applications to risk theory. Insur.: Math. Econ., 2014, vol. 59, pp. 325--336. DOI: https://doi.org/10.1016/j.insmatheco.2014.09.012
[13] Shanmugam R. Informatics about fear to report rapes using bumped-up Poisson model. Amer. J. Biostat., 2013, vol. 3, iss. 1, pp. 17--29. DOI: https://doi.org/10.3844/amjbsp.2013.17.29
[14] Menyaev Yu.A., Nedosekin D.A., Sarimollaoglu M., et al. Optical clearing in photoacoustic flow cytometry. Biomed. Opt. Express, 2013, vol. 4, iss. 12, pp. 3030--3041. DOI: https://doi.org/10.1364/BOE.4.003030
[15] Menyaev Yu.A., Carey K.A., Nedosekin D.A., et al. Preclinical photoacoustic models: application for ultrasensitive single cell malaria diagnosis in large vein and artery. Biomed. Opt. Express, 2016, vol. 7, iss. 9, pp. 3643--3658. DOI: https://doi.org/10.1364/BOE.7.003643
[16] Sitek A., Celler A.M. Limitations of Poisson statistics in describing radioactive decay. PM, 2015, vol. 31, iss. 8, pp. 1105--1107. DOI: https://doi.org/10.1016/j.ejmp.2015.08.015
[17] Menyaev Yu.A., Zharov V.P. Experience in development of therapeutic photomatrix equipment. Biomed. Eng., 2006, vol. 40, iss. 2, pp. 57--63. DOI: https://doi.org/10.1007/s10527-006-0042-6
[18] Menyaev Yu.A., Zharov V.P. Experience in the use of therapeutic photomatrix equipment. Biomed. Eng., 2006, vol. 40, iss. 3, pp. 144--147. DOI: https://doi.org/10.1007/s10527-006-0064-0
[19] Knuth D.E. Art of computer programming. Vol. 2. Seminumerical algorithms. Addison-Wesley, 1997.
[20] Knuth D.E. Art of computer programming. Vol. 4A. Combinatorial Algorithms. P. 1. Addison-Wesley, 2011.
[21] Kolmogorov A.N., Fomin S.V. Elements of the theory of functions and functional analysis. Dover Publ., 1999.
[22] Deon A.F., Menyaev Yu.A. The complete set simulation of stochastic sequences without repeated and skipped elements. J. Univers. Comput. Sci., 2016, vol. 22, iss. 8, pp. 1023--1047. DOI: https://doi.org/10.3217/jucs-022-08-1023
[23] Deon A.F., Menyaev Yu.A. Parametrical tuning of twisting generators. J. Comp. Sci., 2016, vol. 12, iss. 8, pp. 363--378. DOI: https://doi.org/10.3844/jcssp.2016.363.378
[24] Deon A.F., Menyaev Yu.A. Twister generator of arbitrary uniform sequences. J. Univers. Comput. Sci., 2017, vol. 23, iss. 4, pp. 353--384. DOI: https://doi.org/10.3217/jucs-023-04-0353
[25] Deon A.F., Menyaev Yu.A. Uniform twister plane generator. J. Comp. Sci., 2018, vol. 14, iss. 2, pp. 260--272. DOI: https://doi.org/10.3844/jcssp.2018.260.272