Belief Network 0 nodes
Bayesian Networks
A Bayesian network is a probabilistic graphical model representing random variables and their conditional dependencies via a directed acyclic graph.
How to Use
- Set Evidence: Click a value label (left side) to observe that value
- Query: Click a percentage (right side) to see how it was calculated
- Watch posteriors update as you add evidence
Key Concepts
- CPT: Conditional Probability Table (given values in each node)
- Evidence: Observed values (green/red highlighting)
- Posterior: P(Query | Evidence) - computed via inference
Things to Try
Alarm Network
A burglar or earthquake triggers an alarm, which may cause John and Mary to call.
- Set John Calls = True. Watch P(Burglary) increase.
- Also set Mary Calls = True. P(Burglary) increases more!
- Now set Earthquake = True. P(Burglary) drops (explaining away).
Rain/Sprinkler
Classic "explaining away" example.
- Set Wet Grass = True. Both P(Rain) and P(Sprinkler) increase.
- Now set Rain = True. P(Sprinkler) decreases!