Assess Overall Parameter Uncertainty with PSA

Probabilistic Sensitivity Analysis (PSA) –
Analyze how parameter uncertainty affects model results & conclusions

  • Input Uncertainty: Create distributions to represent uncertainty related to specific inputs.
  • PSA Analysis: Sample a set input values from distributions, then run the model using those samples. Repeat this many times to generate a large set of results representing a wide range or parameter combinations.
  • PSA Interpretation: Examine the set of results to assess confidence in the base case model conclusions. Some individual model calculations within the PSA will confirm your conclusions – the higher the percentage, the more confidence we have.

Click here for detailed product documentation on PSA.

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