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• Kinetic Monte Carlo (KMC)
• Probability Distribution
• Computational Mathematics

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## Kinetic Monte Carlo (KMC)

This is a Monte Carlo technique that is applied in the simulation of a natural process that evolves with time. Kinetic Monte Carlo simulates processes that have known rates of transition among states. Also, you should note that the aforementioned rates cannot be predicted by the method itself. Instead, they are the inputs to the Kinetic Monte Carlo algorithm. KMC uses the same principles and works the same as the Gillespie algorithm and the dynamic Monte Carlo method.

## Probability Distribution

A probability distribution is used to determine the possible values and chances that can be taken by a random variable within a particular range. The given range is minimum and maximum values bound. However, there are a plethora of factors that will determine the precise area the possible value is likely to be plotted on the distribution. Some of these factors include skewness, standard deviation, and kurtosis.

## Computational Mathematics

Computational mathematics involves using the concepts of mathematics and mathematical research in fields where computers play a vital role. Some of the applications of computational mathematics are in science and engineering. For example, we require in-depth knowledge of computational mathematics to develop effective and efficient methods for analyzing numerical approximations, calculate integral and differential equations, etc. Computational mathematics requires exceptional skills and a wealth of knowledge of various branches of mathematics. This will guarantee the development of innovative numerical techniques.