Knowledge Modeling with BayesiaLab

Knowledge Modeling with BayesiaLab


Is expert knowledge obsolete?

In today’s business environment that strives to be “data-driven”, expert knowledge seems to be perceived more and more as qualitative or “soft” knowledge. With billions of “hard” data points being accumulated every second, what cannot be counted may not count for much these days, right? A lifetime of experience in any particular domain may appear insignificant in comparison to the huge quantities of newly generated data.

Causal reasoning still requires theory!

This mindset has a critical flaw, which is that causal relationships cannot be machine-learned from data. Rather, causal reasoning always requires some form of assumptions, i.e. assumptions coming from human expertise.

Bayesian networks for knowledge representation

Experts often express causal paths in the form of graphs. This visual representation of causes and effects has a direct analogue in a Bayesian network graph in BayesiaLab. Nodes (representing variables) can be added and positioned with a mouse-click, arcs (representing relationships) can be “drawn” between nodes. The causal direction can be encoded in the direction of the arc.

The quantitative nature of dependencies, plus many other attributes can be managed in the Node Editor, which is available by right-clicking any node. BayesiaLab thus facilitates intuitively encoding one’s own understanding of a domain with a minimum of effort. Simultaneously, it enforces internal consistency, so that no impossible conditions are accidentally encoded.

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