An Alternative Method for Generating Almost Infinite Sequence of Gaussian Variables
Most of the well known methods for generating
Gaussian variables require at least one standard uniform distributed
value, for each Gaussian variable generated. The length of the
random number generator therefore, limits the number of
independent Gaussian distributed variables that can be generated
meanwhile the statistical solution of complex systems requires a
large number of random numbers for their statistical analysis. We
propose an alternative simple method of generating almost infinite
number of Gaussian distributed variables using a limited number of
standard uniform distributed random numbers.
[1] James E. Gentle. Random Number Generation and Monte Carlo
Methods, Series: Statistics and Computing. 2nd ed. 2003. Corr. 2nd
printing, 2003, XV, 300 p., Hardcover. ISBN: 978-0-387-00178-4
[2] Nyah. C. Temaneh, "Monte-Carlo Technique Estimation of a
Probability of Intermodulation Interference in a Cellular Wireless
Communication Network", Proceedings 2010 IEEE Region 8
International Conference on Computational Technologies in Electrical
and Electronics Engineering, Irkutsk, Russia 2010, pp. 329 - 334.
[3] Nyah. C. Temaneh, K. E. Vinogradov, A. N. Krenev, "Monte-Carlo
based estimation of probabilistic characteristics of signal to noise ratio
with GSM 900 cellular communication network as case study (in
Russian)", in Proceedings of IX international scientific - technical
conference on Radiolocation, Navigation and Communication,
Voronesh (Russia), vol. 2, 2005, pp. 1182 - 1188.
[4] Nyah. C. Temaneh, "Estimation of a probability of interference in a
cellular communication network using the Monte Carlo Technique."
Proceedings of the Southern African Telecommunications and
Networks Conference, SATNAC 2009, Swaziland, September 2009.
[5] ERC Report 68,"Monte-Carlo Simulation Methodology for the use in
sharing and compatibility studies between different radio services or
systems" Naples, February 2000
[6] D. H. Lehmer. Mathematical methods in large-scale computing units. In
Proc. 2nd Sympos. On Large Scale Digital Calculating Machinery,
Cambridge, MA, 1949, PP. 141-146, Cambridge, MA, 1951. Harvard
University Press.
[7] Gurskiy E. I., Probability theory with elements of
mathematical statistics. Moscow, 1971.
[1] James E. Gentle. Random Number Generation and Monte Carlo
Methods, Series: Statistics and Computing. 2nd ed. 2003. Corr. 2nd
printing, 2003, XV, 300 p., Hardcover. ISBN: 978-0-387-00178-4
[2] Nyah. C. Temaneh, "Monte-Carlo Technique Estimation of a
Probability of Intermodulation Interference in a Cellular Wireless
Communication Network", Proceedings 2010 IEEE Region 8
International Conference on Computational Technologies in Electrical
and Electronics Engineering, Irkutsk, Russia 2010, pp. 329 - 334.
[3] Nyah. C. Temaneh, K. E. Vinogradov, A. N. Krenev, "Monte-Carlo
based estimation of probabilistic characteristics of signal to noise ratio
with GSM 900 cellular communication network as case study (in
Russian)", in Proceedings of IX international scientific - technical
conference on Radiolocation, Navigation and Communication,
Voronesh (Russia), vol. 2, 2005, pp. 1182 - 1188.
[4] Nyah. C. Temaneh, "Estimation of a probability of interference in a
cellular communication network using the Monte Carlo Technique."
Proceedings of the Southern African Telecommunications and
Networks Conference, SATNAC 2009, Swaziland, September 2009.
[5] ERC Report 68,"Monte-Carlo Simulation Methodology for the use in
sharing and compatibility studies between different radio services or
systems" Naples, February 2000
[6] D. H. Lehmer. Mathematical methods in large-scale computing units. In
Proc. 2nd Sympos. On Large Scale Digital Calculating Machinery,
Cambridge, MA, 1949, PP. 141-146, Cambridge, MA, 1951. Harvard
University Press.
[7] Gurskiy E. I., Probability theory with elements of
mathematical statistics. Moscow, 1971.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59604", author = "Nyah C. Temaneh and F. A. Phiri and E. Ruhunga", title = "An Alternative Method for Generating Almost Infinite Sequence of Gaussian Variables", abstract = "Most of the well known methods for generating
Gaussian variables require at least one standard uniform distributed
value, for each Gaussian variable generated. The length of the
random number generator therefore, limits the number of
independent Gaussian distributed variables that can be generated
meanwhile the statistical solution of complex systems requires a
large number of random numbers for their statistical analysis. We
propose an alternative simple method of generating almost infinite
number of Gaussian distributed variables using a limited number of
standard uniform distributed random numbers.", keywords = "Gaussian variable, statistical analysis, simulation ofCommunication Network, Random numbers.", volume = "5", number = "2", pages = "200-5", }