Graphical models
Description
An introduction to directed and undirected probabilistic graphical models, including inference (belief propagation and the junction tree algorithm), parameter learning and structure learning, variational approximations, and approximate inference.
- Introduction to graphical models: (directed, undirected and factor graphs; conditional independence; d-separation; plate notation)
- Inference and propagation algorithms: (belief propagation; factor graph propagation; forward-backward and Kalman smoothing; the junction tree algorithm)
- Learning parameters and structure: maximum likelihood and Bayesian parameter learning for complete and incomplete data; EM; Dirichlet distributions; score-based structure learning; Bayesian structural EM; brief comments on causality and on learning undirected models)
- Approximate Inference: (Laplace approximation; BIC; variational Bayesian EM; variational message passing; VB for model selection)
- Bayesian information retrieval using sets of items: (Bayesian Sets; Applications)
- Foundations of Bayesian inference: (Cox Theorem; Dutch Book Theorem; Asymptotic consensus and certainty; choosing priors; limitations)
| Slides | |
| 0:00 | Lecture 1: Introduction to Graphical Models |
| 0:00 | Lecture 1: Introduction to Graphical Models |
| 2:13 | Three main kinds of graphical models |
| 3:44 | Why do we need graphical models? |
| 6:20 | Conditional Independence |
| 8:50 | Conditional and Marginal Independence (Examples) |
| 12:00 | Factor Graphs |
| 14:24 | Factor Graphs01 |
| 16:21 | Proving Conditional Independence |
| 20:03 | Undirected Graphical Models |
| 22:38 | Undirected Graphical Models01 |
| 25:43 | Comparing Undirected Graphs and Factor Graphs |
| 28:47 | Problems with Undirected Graphs and Factor Graphs |
| 36:08 | Directed Acyclic Graphical Models (Bayesian Networks) |
| 37:50 | Directed Acyclic Graphical Models (Bayesian Networks)01 |
| 41:32 | Examples of D-Separation in DAGs |
| 47:15 | From Directed Trees to Undirected Trees |
| 49:20 | Directed Graphs for Statistical Models: Plate Notation |
| 50:27 | Expressive Power of Directed and Undirected Graphs |
| 52:15 | Summary |
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32 comments
Watch videos: (click on thumbnail to launch)
Part 1
0:53:16
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Part 2
0:58:19
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Part 3
1:18:43
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Part 4
0:31:00
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Part 5
1:05:20
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Part 6
0:29:16
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Will there be a version in Window Media Format?
Part 2 (file suffix 01) ends at about 32% (wmv). Do others have the same problem?
I have checked all the parameters and asked students from CMU if they have troubles with this video, but they dont seem to have this problem. Have you checked your players settings?
Will the other parts of the tutorials be available soon?
zhoubin's talk is very nice! But the third part about learning the parameters and structures of directed graphical model is missing! Can this part of talk be available?
I am specifically looking for the part on learning the parameters and structures... Unfortunately this seems to be missing :(. Please let me know where I can find it!
Great talk! I find it very useful.
Hi,
i was wondering where the first part of lecture 3 is?, could you please upload that,..Thanks,
Great comprehensive lecture.
Many thanks to prof. Ghahramani.
Hello everyone, we have added a new version of part 3 and 4 so enjoy them.