• A probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. -Wikipedia

Is a:

• method

Applications:

• Anomaly Detection
• Inference
• Spam Filtering
• Time Series Forecasting

Advantages:

• can encode conditional independencies

Disadvantages:

• cannot automatically learn the structure of data; must be encoded manually
• must assume prior distributions of variables