edit

Information Gap Analysis

Information Gap Analysis

All the figures below are generated using examples/model_analysis/infogap.jl.

Setup

  • There are 4 uncertain observations at times t = [1,2,3,4]

  • There are 4 possible models that can be applied to match the data

    1. y(t) = a * t + c

    2. y(t) = a * t^(1.1) + b * t + c

    3. y(t) = a * t^n + b * t + c

    4. y(t) = a * exp(t * n) + b * t + c

  • There are 4 unknown model parameters with uniform prior probability functions:

    1. a = Uniform(-10, 10)

    2. b = Uniform(-10, 10)

    3. c = Uniform(-5, 5)

    4. n = Uniform(-3, 3)

  • The model prediction for t = 5 is unknown and information gap prediction uncertainty needs to be evaluated

  • The horizon of information gap uncertainty h is applied to define the acceptable deviations in the 4 uncertain observations.

Infogap in model y(t) = a * t + c

Infogap in model y(t) = a * t^(1.1) + b * t + c

Infogap in model y(t) = a * t^n + b * t + c

Infogap in model y(t) = a * exp(t * n) + b * t + c

Opportuneness and Robustness of the 4 models