# What is Monte Carlo simulation?

Category:
 ▲ 0 ▼ ♥ 0 What is Monte Carlo simulation?
Explanation Monte Carlo method perform risk analysis by building models of possible outcomes, then substituting with a range of values(a probability distribution) for factors with inherent uncerta...

Explanation

Monte Carlo method perform risk analysis by building models of possible outcomes, then substituting with a range of values(a probability distribution) for factors with inherent uncertainty.
It then calculate results again and again, every moment using a different set of random value from probability functions. Based on number of the uncertainties and their specified range, Monte Carlo simulation may involve thousands and more recalculations before it is finished, which will in the end come up with possible outcome values.
In using probability distributions, variables bear different probability of different outcomes happening.
Probability distributions stand more real in explaining uncertainty in variable of risk analysis.

Monte Carlo simulation / Monte Carlo method refers to a computerized mathematical technique which allows individuals to account for risks in qualitative analysis and come up with decisions.
This technique is used by professionals in the field of finance, project management, insurance, engineering, research, just to name a few, whereby the method allows them to see all the possible outcomes of their decisions and be able to assess the impact of risk, thus allowing for better decision making under uncertainty.
Monte Carlo simulation furnishes decision makers with a wide range of possible outcomes and probability that they will occur, based on action taken.
This method shows the possibilities and outcomes of going broke and renders possible consequences for any given decisions stage.
Monte Carlo simulation has a broad class of computational algorithms which rely on repeated random sampling to obtain numerical results. Underlying concept is to engage randomness to solve problems that are deterministic in principle.