# 2007 PracticalStatisticalAI

- (Domingos, 2007) ⇒ Pedro Domingos. (2007). “Practical Statistical Relational AI.” In: Tutorial at AAAI 2007 Conference.

**Subject Headings:** Statistical Relational Model, Markov Network

## Notes

## Cited By

~92 http://scholar.google.com/scholar?cites=8848896538786858875

## Quotes

### Plan

- We have the elements:
- Probability for handling uncertainty
- Logic for representing types, relations, and complex dependencies between them
- Learning and inference algorithms for each

### Hammersley-Clifford Theorem

- If Distribution is strictly positive (P(x) > 0)
- And Graph encodes conditional independences
- Then Distribution is product of potentials over cliques of graph
- Inverse is also true.

(“Markov network = Gibbs distribution”

### Markov Nets vs. Bayes Nets

| Property | Markov Nets | Bayes Nets| | Form | Prod. potentials | Prod. potentials| | Potentials | Arbitrary | Cond. probabilities| | Cycles | Allowed | Forbidden| | Partition func. | Z = ? | Z = 1| | Indep. check | Graph separation | D-separation| | Indep. props. | Some | Some| | Inference | MCMC, BP, etc. | Convert to Markov|

## References

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