Simulation using random numbers

TL;DR

This paper explores the use of Monte Carlo methods to construct and analyze stochastic models, particularly focusing on Brownian motion, to enhance understanding of probability distributions like uniform and normal.

Contribution

It introduces a Monte Carlo-based approach for modeling Brownian motion and demonstrates its effectiveness in illustrating key probability distributions.

Findings

Monte Carlo methods effectively model Brownian motion.

The approach clarifies the properties of uniform and normal distributions.

Simulation results support the theoretical foundations of stochastic processes.

Abstract

This article is devoted to methods of construction and study of stochastic models based on Monte Carlo method. A model of Brownian motion, the construction and processing which brings to a world of random numbers and mathematical statistics, promotes understanding of the probability distribution, in particular illustrates two common distributions: uniform and normal.