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:

1. Probability Concepts in Simulation- Stochastic Variable
2. Discrete Probability Function
3. Cumulative Distribution Function
4. Continuous Probability Function
5. Random Variables
6. Discrete Random Variable
7. Continuous Random Variable
8. Random Numbers
9. Properties of Random Numbers
10. Pseudo-Random Numbers
11. Generation of Random Number
12. Qualities of an Efficient Random Number Generator
13. Techniques for Generating Random Numbers
14. Linear Congruential Method (LCM)
15. Combined Linear Congruential Generators (CLCG)
16. Tests for Random Numbers
17. Testing for Uniformity
18. Testing for Independence
19. Frequency Tests
20. Kolmogorov-Smirnov Test
21. Chi-Square Test
22. Runs Tests
23. Runs Up And Down
24. Runs Above And Below The Mean
25. Runs Test: Length Of Runs
26. Test For Autocorrelation

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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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