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.

Probability Concept and Random Number Generation - Simulation and Modeling

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

==== 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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