Study of Monte Carlo Simulation: Principles, Methods, and Applications

Jiajie Li

doi:10.54097/sev38v22

Authors

Jiajie Li

DOI:

https://doi.org/10.54097/sev38v22

Keywords:

Monte Carlo Simulation, Probability, Statistics, Python, Modeling.

Abstract

Monte Carlo Simulation (MCS) is a powerful computational technique used to model complex stochastic systems, enabling the evaluation of probabilities and statistical outcomes through random sampling. Originating from the field of physics, MCS has since become a versatile tool in various disciplines, including finance, engineering, and risk management. This paper explores the historical development, core principles, and mathematical foundations of MCS, as well as its practical applications. Additionally, the paper highlights the importance of programming in implementing Monte Carlo methods, particularly through Python's random package. Three specific examples demonstrate the simulation process, allowing for the evaluation of the effectiveness and efficiency of random processes in generating desired results. The study also presents an overview of MCS's applicability in real-world scenarios, offering insights into its advantages and limitations. Through this exploration, the paper aims to provide a comprehensive understanding of the Monte Carlo simulation and its relevance in modern computational modeling.

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