Conditional Probability Table

Conditional Probability Table (CPT)


  • Bayesian networks are models that consist of two parts:
    1. A qualitative part to represent the dependencies using a Directed Acyclic Graph (DAG).
    2. A quantitative part, using local probability distributions, for specifying the probabilistic relationships.
  • A Directed Acyclic Graph (DAG) consists of nodes and directed links:
    • Nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, or the occurrence of an event).
    • Nodes can correspond to symbolic/categorical variables, numerical variables with discrete values, or discretized continuous variables.
    • Directed arcs represent statistical (informational) or causal dependencies among the variables. The directions are used to define kinship relations, i.e., parent-child relationships.
    • For example, in a Bayesian network with an arc from X to Y, X is the parent node of Y, and Y is the child node.
    • The local probability distributions can be either marginal for nodes without parents (Root Nodes) or conditional for nodes with parents.
    • In the latter case, the dependencies are quantified by Conditional Probability Tables (CPT) for each node given its parents in the Directed Acyclic Graph (DAG).
    • Once fully specified, a Bayesian network compactly represents the Joint Probability Distribution (JPD).
    • Thus, the Bayesian network can be used for computing the posterior probabilities of any subset of nodes given evidence set on any other subset.


  • The following illustration shows a simple Bayesian network, which consists of only two nodes and one directed arc.
  • This Bayesian network represents the Joint Probability Distribution (JPD) of the variables Eye Color and Hair Color in a population of students (Snee, 1974 (opens in a new tab)).
  • Eye Color is a Root Node and, therefore, does not have any Parents. In other words, Eye Color does not depend on any other node. _ As a result, the table associated with Eye Color is a Probability Table, i.e., it represents the marginal distribution of Eye Color unconditionally. _ On the other hand, the probabilities of Hair Color are only defined conditionally upon the values of its parent node, Eye Color.
  • Hence, the probabilities of Hair Color are provided in a Conditional Probability Table (CPT).
  • It is important to point out that this Bayesian network does not imply any causal relationships, even though the arc direction may suggest that to a casual observer.

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