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/Transform Descriptive Graphs into Bayesian Networks
Transform Descriptive Graphs into Bayesian Networks
Explore directed, acyclic graphs for causal relationships and identify relationships between random variables.
In this lesson, we will discuss the process of converting a descriptive graph into a Bayesian network, a probabilistic graphical model that represents the relationships between random variables and their probabilities.
First, let's recall some important concepts linked to the concept of BN:
Directed graph: The edges in a Bayesian network are directed, meaning they have a specific direction, originating from one node (the parent node) and pointing towards another node (the child node). This directionality indicates the flow of influence or causality between the nodes. A directed edge from node A to node B implies that the probability distribution of node B depends on the value of node A. The directed nature of the edges allows us to ...