Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings

Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings

ISBN-10:
3319283782
ISBN-13:
9783319283784
Pub. Date:
12/23/2023
Publisher:
Springer International Publishing
ISBN-10:
3319283782
ISBN-13:
9783319283784
Pub. Date:
12/23/2023
Publisher:
Springer International Publishing
Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings

Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings

$54.99 Current price is , Original price is $54.99. You
$54.99 
  • SHIP THIS ITEM
    Not Eligible for Free Shipping
  • PICK UP IN STORE
    Check Availability at Nearby Stores

Overview

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.



Product Details

ISBN-13: 9783319283784
Publisher: Springer International Publishing
Publication date: 12/23/2023
Series: Lecture Notes in Computer Science , #9505
Edition description: 1st ed. 2015
Pages: 265
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of 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.
From the B&N Reads Blog

Customer Reviews