1 option
Advanced Methodologies for Bayesian Networks : Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings / edited by Joe Suzuki, Maomi Ueno.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 9505
- Lecture Notes in Artificial Intelligence, 2945-9141 ; 9505
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Algorithms.
- Computer science-Mathematics.
- Mathematical statistics.
- Computer science.
- Database management.
- Application software.
- Artificial Intelligence.
- Probability and Statistics in Computer Science.
- Theory of Computation.
- Database Management.
- Computer and Information Systems Applications.
- Local Subjects:
- Artificial Intelligence.
- Algorithms.
- Probability and Statistics in Computer Science.
- Theory of Computation.
- Database Management.
- Computer and Information Systems Applications.
- Physical Description:
- 1 online resource (XVIII, 265 pages) : 102 illustrations in color.
- Edition:
- 1st ed. 2015.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2015.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- Effectiveness of graphical models including modeling. Reasoning, model selection
- Logic-probability relations
- Causality. Applying graphical models in real world settings
- Scalability
- Incremental learning.-Parallelization.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-28379-1
- 9783319283791
- Access Restriction:
- Restricted for use by site license.
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