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
• Reasoning in Bayesian nets
• A Practical Example