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teaching:ss2019:pgm2019 [2019/05/24 16:15] Zhenzhang Ye |
teaching:ss2019:pgm2019 [2019/07/01 19:18] wuta |
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<b> Announcement: | <b> Announcement: | ||
+ | The lecture on 22.07 will be on deep Boltzmann machines presented by [[: | ||
+ | There will be NO tutorial on Wednesday, 12.06.2019. Sheet5 should be submitted on 17.06.< | ||
There will be NO lecture on Wednesday, 24.04.2019. | There will be NO lecture on Wednesday, 24.04.2019. | ||
</b> | </b> | ||
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- Graphical model inference: | - Graphical model inference: | ||
* Variable elimination | * Variable elimination | ||
- | * Sum-product algorithm | ||
* Junction-tree algorithm | * Junction-tree algorithm | ||
+ | * Belief propagation | ||
* Graph cut and move-making extensions | * Graph cut and move-making extensions | ||
* Linear programming relaxation | * Linear programming relaxation | ||
- Approximative inference techniques: | - Approximative inference techniques: | ||
- | * Loopy belief propagation, message passing | + | * Loopy belief propagation |
* Mean field, variational inference | * Mean field, variational inference | ||
* Sampling methods | * Sampling methods |