Fabio Cozman, Denis Deretani Maua
Credal networks: specifications, algorithms and complexity
The slides of the lecture can be accessed here
Abstract
Credal networks generalize Bayesian networks to allow for imprecision in probability values. This tutorial reviews the main results on credal networks, in particular under strong independence, as there has been significant progress in the literature during the last decade or so. We focus on computational aspects, summarizing the main algorithms and complexity results for inference and decision making. We address the question “What is really known about strong and epistemic extensions of credal networks?” by looking at theoretical results and by presenting a short summary of real applications.
Syllabus
| 11:00 - 13:00 |
1) First part: Basic concepts |
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1.1) Credal sets, graphs and networks. |
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1.2) A bit of history. |
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1.3) Strong and epistemic extensions. |
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1.4) Examples and exercises. |
| 13:30 - 15:00 |
2) Second part: Advanced topics |
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2.1) Algorithms for marginal inference and decision making. |
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2.2) The complexity of marginal inference and decision making. |
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2.3) Eliciting, learning, and applying credal networks. |
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3) Conclusion |