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Learning bounded tr...
Learning bounded tree-width Bayesian networks using integer linear programming
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- Parviainen, Pekka (author)
- KTH,Science for Life Laboratory, SciLifeLab,SeRC - Swedish e-Science Research Centre
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Farahani, H. S. (author)
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- Lagergren, Jens (author)
- KTH,Science for Life Laboratory, SciLifeLab,SeRC - Swedish e-Science Research Centre
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(creator_code:org_t)
- Microtome Publishing, 2014
- 2014
- English.
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In: Journal of machine learning research. - : Microtome Publishing. - 1532-4435 .- 1533-7928. ; 33, s. 751-759
- Related links:
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https://urn.kb.se/re...
Abstract
Subject headings
Close
- In many applications one wants to compute conditional probabilities given a Bayesian network. This inference problem is NP-hard in general but becomes tractable when the network has low tree-width. Since the inference problem is common in many application areas, we provide a practical algorithm for learning bounded tree-width Bayesian networks. We cast this problem as an integer linear program (ILP). The program can be solved by an anytime algorithm which provides upper bounds to assess the quality of the found solutions. A key component of our program is a novel integer linear formulation for bounding tree-width of a graph. Our tests clearly indicate that our approach works in practice, as our implementation was able to find an optimal or nearly optimal network for most of the data sets.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- Artificial intelligence
- Forestry
- Inference engines
- Integer programming
- Learning algorithms
- Optimization
- Trees (mathematics)
- Any-time algorithms
- Application area
- Conditional probabilities
- Inference problem
- Integer Linear Programming
- Integer linear programs
- Linear formulation
- Optimal networks
- Bayesian networks
Publication and Content Type
- ref (subject category)
- art (subject category)
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