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Module 1: Information and Probabilistic Modelling

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Information and Probabilistic Modelling - Lesson Summary

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Information and Uncertainty

Information refers to the intelligence or idea or message in Information Theory. Information comes from measurements, tests or experiments. Uncertainty refers to epistemic situations involving imperfect or unknown information.

The concepts of Uncertainty and Information are highly interconnected. Uncertainty is viewed as a manifestation of some information deficiency, while Information is viewed as the capacity to reduce uncertainty.

Model Uncertainties

Model Uncertainties relate to “gaps in scientific knowledge that hamper an adequate capture of the correct causal relations between exposure factors.

Decision-makers are increasingly willing to consider the uncertainty associated with model predictions of the impacts of their possible decisions.

Information on Uncertainty does not make the decision–making easier, but to ignore it is to ignore reality.

Incorporating what is known about the Uncertainty into input parameters and variables used in optimization and simulation models can help in quantifying the Uncertainty in the resulting model predictions.

Probability Model

A Probability Model is a mathematical representation of a random phenomenon. It is defined by its sample space, events within the sample space, and probabilities associated with each event.

Random Variable

A Random Variable, random quantity, aleatory variable, or stochastic variable is described informally as a variable whose values depend on outcomes of a random phenomenon.

Limit Theorems

Limit Theorems in probability theory give conditions for the appearance of some regularity as the result of the action of a large number of random sources.