Calculating risk probability software#

The uniform distribution assumes that values are equally distributed between a lower and upper boundary, which requires the expert to estimate a lower and upper boundary as the input. Commonly used distributions for Monte Carlo simulations are the uniform distribution, triangular distribution, and normal distribution. For each input a statistical distribution is chosen. It makes it easier for experts to provide these quantitative inputs. The Monte Carlo simulation allows the collection of quantitative inputs for TIRM process steps B1 to B7 in the form of a probability distribution, instead of using exact values, which are often difficult to get. The model, as presented in Chapter 5, is used to make the risk calculations for TIRM process step B9. The average, variance, and confidence intervals can be then calculated to summarize the results of these calculations. The simulation generates N random numbers that follow the defined distribution for each of the elements and executes the calculations N times. Then, mathematical calculations are defined that should be executed on these elements and the number N of simulation runs has to be determined.

First, the elements that should be represented and appropriate distributions are chosen for the simulation. The Monte Carlo simulation uses algorithms that can be run on any computer that creates a large amount of random numbers of a chosen distribution.

Calculating risk probability software#

Many software tools are available to assist in helping build Monte Carlo simulations, such as the TIRM pilot software tool presented in Chapter 12. The Monte Carlo simulation is a method that allows you to obtain results when modeling the problem mathematically and/or finding that an analytical solution is too complex. Philip Woodall, in Total Information Risk Management, 2014 Monte carlo simulation