Advanced Methodologies for Bayesian Networks
Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Programming
| Author: | ISBN: | 9783319283791 | |
| Publisher: | Springer International Publishing | Publication: | January 7, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | |
| ISBN: | 9783319283791 |
| Publisher: | Springer International Publishing |
| Publication: | January 7, 2016 |
| Imprint: | Springer |
| Language: | English |
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.
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.
