Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty.

A random number is a number generated by a process, whose outcome is unpredictable, and which cannot be subsequentially reliably reproduced. Random numbers are the basic building blocks for all simulation algorithms.

In this “Probability Concept and Random Number Generation - Simulation and Modeling” you will learn about following topics:

- Probability Concepts in Simulation- Stochastic
Variable
- Discrete Probability Function
- Cumulative Distribution Function
- Continuous Probability Function
- Random Variables
- Discrete Random Variable
- Continuous Random Variable
- Random Numbers
- Properties of Random Numbers
- Pseudo-Random Numbers
- Generation of Random Number
- Qualities of an Efficient Random Number Generator
- Techniques for Generating Random Numbers
- Linear Congruential Method (LCM)
- Combined Linear Congruential Generators (CLCG)
- Tests for Random Numbers
- Testing for Uniformity
- Testing for Independence
- Frequency Tests
- Kolmogorov-Smirnov Test
- Chi-Square Test
- Runs Tests
- Runs Up And Down
- Runs Above And Below The Mean
- Runs Test: Length Of Runs
- Test For Autocorrelation

==== Point to Note ====

This article Probability Concept and Random Number Generation - Simulation and Modeling is contributed by Pawan Tiwari, a student of LA GRANDEE International College (LGIC).

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