GoldSim allows you to utilize an importance sampling algorithm to modify the conventional Monte Carlo approach so that selected portions of input distributions (which could correspond to high-consequence, low-probability outcomes) are sampled with an enhanced frequency. This importance sampling approach is often necessary to evaluate the low-probability, high-consequence portions of the probability distribution.
This model samples a function that has high result values when random variable Stoch1 has a value close to 7.93543, its 40% quantile.
If weights are not applied (DoWeights is False) the Result Distribution shown above will not be valid. However, the result to the left will then display the distribution of random numbers that was used for the simulation (illustrating the biasing function). This result is only meaningful if DoWeights is False.
The resulting conditional weight is used in the simulation settings of the model.
To Open the Model File:
- Start GoldSim
- Click on the File and select Open Example...
- Browse to General Examples --> Stochastic
- Select the file called CustomImportance.gsm