Meeting 19: Bayesian Networks
Reading: AIAMA 14.1-14.4.1
Probabalistic agent models and algorithms for doing inference on them can get quite complex. A useful way to develop, visualize, describe, and represent such models is through a graphical interpretation called Bayesian Networks (BN) or Belief Networks.
Questions you should be able to answer after reading are:
- How does one model an uncertain environment using a BN?
- What do the nodes of a BN represent?
- What do the edges/connections in a BN represent?
- How do you go back and forth between a BN and the model joint distribution?
- Why are models resulting in sparse graphs desirable?
- What does conditional independence in a BN mean?
- What is the Markov blanket?
- What information and data structures are needed to represent a BN?
- How does one do inference using enumeration?
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