Bayesian networks

These are also called Belief Networks or Probabilistic Inference Networks. Initially developed by Pearl (1988).
The basic idea is:

• Knowledge in the world is modular -- most events are conditionally independent of most other events.
• Adopt a model that can use a more local representation to allow interactions between events that only affect each other.
• Some events may only be unidirectional others may be bidirectional -- make a distinction between these in model.
• Events may be causal and thus get chained together in a network.

Implementation

• A Bayesian Network is a directed acyclic graph:
  • A graph where the directions are links which indicate dependencies that exist between nodes.
  • Nodes represent propositions about events or events themselves.
  • Conditional probabilities quantify the strength of dependencies.

Consider the following example:

• The probability,  that my car won't start.
• If my car won't start then it is likely that
  • The battery is flat or
  • The staring motor is broken.