Advanced methodologies for Bayesian networks : second international workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings / Joe Suzuki, Maomi Ueno (eds.).
Material type:
TextSeries: Lecture notes in computer science. Lecture notes in artificial intelligence ; ; 9505. | LNCS sublibrary. SL 7, Artificial intelligence.Publisher: Cham : Springer, 2015Description: 1 online resource (xviii, 265 pages) : color illustrationsContent type: - text
- computer
- online resource
- 9783319283791
- 3319283790
- AMBN 2015
- Artificial intelligence -- Congresses
- Bayesian statistical decision theory -- Congresses
- Intelligence artificielle -- Congrès
- Théorie de la décision bayésienne -- Congrès
- Algorithms & data structures
- Maths for computer scientists
- User interface design & usability
- Databases
- Information retrieval
- Artificial intelligence
- Computers -- Programming -- Algorithms
- Computers -- Mathematical & Statistical Software
- Computers -- Machine Theory
- Computers -- Database Management -- General
- Computers -- Information Technology
- Computers -- Intelligence (AI) & Semantics
- Artificial intelligence
- Bayesian statistical decision theory
- 006.3 23
- Q334
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eBook
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e-Library | eBook LNCS | Available |
International conference proceedings.
Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed January 15, 2016).
Effectiveness of graphical models including modeling. Reasoning, model selection -- Logic-probability relations -- Causality. Applying graphical models in real world settings -- Scalability -- Incremental learning.-Parallelization.
This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015. The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.
English.