Machine Learning Quick Reference

Graphical causal models

This model was covered in detail in Chapter 8, Probabilistic Graphical Models. We will also look into it briefly here, too.

Bayesian networks are directed acyclic graphs (DAGs) where the nodes represent variables of interest (for example, the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event, and so on). Causal influences among the variables are represented using links. The strength of an influence can be potrayed by conditional probabilities that are linked to each cluster of the parent-child nodes in the network. In the following diagram we can see the causal models, that have a node and an edge:

The node represents the variables and the edges stand for conditional ...