Your Guide to Monte Carlo Simulation and Must Know Statistical Sampling Techniques With Python Implementation

onte Carlo Simulation is based on repeated random sampling. The underlying concept of Monte Carlo is to use **randomness **to solve problems that might be deterministic in principle. Monte Carlo simulation is one of the most popular techniques to draw inferences about a population without knowing the true underlying population distribution. This sampling technique becomes handy especially when one doesn’t have the luxury to repeatedly sample from the original population. Applications of Monte Carlo Simulation range from solving problems in theoretical physics to predicting trends in financial investments.

Monte Carlo has 3 main usages: estimate parameters or statistical measures, examine the properties of the estimates, approximate integrals

This article is about these 3 usages of the Monte Carlo procedures and about 3 Monte Carlo variants, statistical sampling techniques, which can be used to generate independent random samples. The article will cover the following topics:

- Introduction to Monte Carlo Simulation
- MC Parameter Estimation
- MC Examining the Estimate Properties
- MC Integrals Approximation
- Importance Sampling
- Rejection Sampling
- Inverse Transform Sampling

_This article is suited for readers who have prior Statistical knowledge since it will cover medium-level statistical concepts and examples. If you want to learn essential statistical concepts from scratch, you can check my previous article about _Fundamentals Of Statistics here.

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