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Tools in Data Driven Decision Making

Updated: Mar 31, 2021

Bayesian Networks


Its most important feature is its ability to calculate likelihood (conditional probability) of the two or more actions (e.g. what will be the likelihood of event C when event B and A has already occurred, and what will be the likelihood if only event A occurs but B fails). The technique is particularly useful on complex systems which has many causes and effects (causal relationships).


Some of the key benefits are; it’s easy to visualize the relationships, different relationships can be chained / combined to allow for more complex situations, which gives room for scaling, and more likelihoods (conditional probabilities) can be calculated.

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