The strong law of large numbers

See also: Probability theory and statistics introduction, Monte Carlo simulation introduction

The strong law of large numbers is the principle upon which Monte Carlo simulation is built. In laymans terms, it says that the larger the sample size (i.e. the greater the number of iterations), the closer their distribution (i.e. the risk analysis output) will be to the theoretical distribution (i.e. the exact distribution of the models output if it could be mathematically derived).

This law is intuitively rather obvious and many text books on probability theory reproduce a proof. It is sufficient for our needs just to say that the above law can be proven.

Read on: Stirlings formula for factorials

 

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