Abstract
Social scientists increasingly use statistical simulation techniques to help them understand the social processes they care about and the statistical methods used to study them. There are two types of computer simulation techniques, which are quickly becoming essential tools for empirical social scientists: Monte Carlo simulation and resampling methods. This chapter is dealing with Monte Carlo method that is very often used for simulating systems with many coupled degrees of freedom, for simulation of experiments in many areas of research, for investigation of processes with a random character and is most useful when it is difficult to use other approaches. The chapter presents some methods of generating random numbers by usage of standard numerical methods for various probability distributions types as well as application possibility of Monte Carlo method for a CA-simulation of the random processes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Čičmanec, L., Bořil, J.: Assessment of tactical mission simulation exercise. In: Transport Means 2017. Kaunas University of Technology, Kaunas, pp. 729–734 (2017). ISSN 1822-296X
Dagpunar, J.S.: Simulation and Monte Carlo: with applications in finance and MCMC. Wiley, ACCEM (2008), New York, (2007), 348 p. ISBN 978-0-470-85494-5
Fishman, G.S.: Monte Carlo Concepts, Algorithms and Applications. Springer, Berlin (1996)
Gentle, J.E.: Random Number Generation and Monte Carlo Methods. Springer, Berlin (2003)
Glasserman, P.: Monte Carlo Methods in Financial Engineering. Springer, Berlin (2003)
Hubacek, M., Vrab, V.: Cost assessment of training using constructive simulation. eBook: Mathematical-Statistical Models and Qualitative Theories for Economic and Social Sciences, vol. 104. Springer International Publishing AG, New York (2017). ISSN 2198-4182
Jirgl, M., Bořil, J., Jalovecký, R.: Assessing quality of pilot training with use of mathematical analyses. In: Proceedings of the 2016 17th International Conference on Mechatronics - Mechatronika (ME), Czech Technical University in Prague, Prague, pp. 91–96 (2016). ISBN 978-80-01-05882-4
Knežo, D.: About the method of Monte Carlo and its applications. Transf. Innov. 24, 178–181 (2012a)
Knežo, D.: Estimation of costs concerning prothetic and orthotic aids using the Monte Carlo method. In: AEI’2012: International Conference on Applied Electrical Engineering and Informatics (2012b)
Knežo, D.: Inverse transformation method for normal distribution and the standard numerical methods. Int. J. Interdiscip. Theory Pract. 5(2014), 6–10 (2014)
Kubíček, P., Šašinka, Č., Stachoň, Z.: Selected cognitive issues of positional uncertainty in geographical data. Geografie - Sborník České geografické společnosti, Česká geografická společnost 119(1), 67–90 (2014). ISSN 1212-0014
McLeish, D.L.: Monte Carlo Simulation and Finance. Wiley, New York (2005)
Sobol, I.M.: Die Monte-Carlo-Methode. VEB Deutscher Verlag der Wissenschaften, Berlin (1971)
Svatoňová, H., Šikl, R.: Cognitive aspects of interpretation of image data. eBook: Mathematical-Statistical Models and Qualitative Theories for Economic and Social Sciences, vol. 104. Springer International Publishing AG, New York (2017) ISSN 2198-4182
Urban, R., Hošková-Mayerová, Š.: Threat life cycle and its dynamics. Deturope 9(2), 93–109 (2017)
Woch, M., Zieja, M.,Tomaszewska, J.: Analysis of the time between failures of aircrafts. In: 2nd International Conference on System Reliability and Science (ICSRS 2017), pp. 112–118 (2017)
Acknowledgements
The work presented in this chapter has been supported by the project KEGA 026TUKE-4/2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Knežo, D., Vagaská, A. (2019). Monte Carlo Method Application and Generation of Random Numbers by Usage of Numerical Methods. In: Flaut, C., Hošková-Mayerová, Š., Flaut, D. (eds) Models and Theories in Social Systems. Studies in Systems, Decision and Control, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-030-00084-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-00084-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00083-7
Online ISBN: 978-3-030-00084-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)