Introduction To Monte Carlo Simulation. A Monte Carlo simulation is simply a method of estimating the value of an unknown quantity using the principles of inferential statistics. This is an introductory tutorial on Monte Carlo simulation a type of simulation that relies on repeated random sampling and statistical analysis to compute the results.
In this tutorial the reader will learn the Monte Carlo methodology and its applications in data science. Monte Carlo estimation refers to simulating hypothetical draws from a probability distribution. How likely each value is governed by the probability density function PDF px.
Monte Carlo Simulation also known as the Monte Carlo Method or a multiple probability simulation is a mathematical technique which is used to estimate the possible outcomes of an uncertain event.
The Monte Carlo Method was invented by John von Neumann and Stanislaw Ulam during World War II to improve decision making under uncertain conditions. One way to capture uncertainties is through Monte Carlo simulation. Since we are dealing with stochastic random processes the calculations are repeated over and over in a typical simulation. Monte Carlo estimation refers to simulating hypothetical draws from a probability distribution.
