Statistical Modeling Of Relational Data00:03
Overview pt 100:09
Motivation00:38
Examples03:37
Costs and Benefits of Multi-Relational Data Mining04:45
Goal and Progress07:27
Plan08:55
Disclaimers10:56
Overview pt 212:10
Markov Networks pt 112:14
Markov Networks pt 215:33
Markov Nets vs. Bayes Nets17:05
Inference in Markov Networks19:20
MCMC: Gibbs Sampling20:41
Other Inference Methods21:47
MAP/MPE Inference22:49
MAP Inference Algorithms23:44
Overview pt 324:59
Learning Markov Networks25:07
Generative Weight Learning25:38
Pseudo-Likelihood28:06
Discriminative Weight Learning30:02
Other Weight Learning Approaches32:25
Structure Learning32:51
Overview pt 434:23
First-Order Logic34:27
Inference in First-Order Logic36:09
Satisfiability36:50
Stochastic Local Search38:29
The WalkSAT Algorithm39:10
Overview pt 540:28
Rule Induction40:40
Learning a Single Rule42:02
Learning a Set of Rules42:43
First-Order Rule Induction44:10
Overview pt 546:46
Plethora of Approaches47:00
Key Dimensions47:25
Knowledge-Based Model Construction48:51
Stochastic Logic Programs50:45
Probabilistic Relational Models52:14
Relational Markov Networks53:59
Bayesian Logic55:09
Markov Logic pt 157:20
Markov Logic pt 258:25
