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Advanced Methodologies for Bayesian Networks

Overview of attention for book
Cover of 'Advanced Methodologies for Bayesian Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Efficiently Learning Bayesian Network Structures Based on the B&B Strategy: A Theoretical Analysis
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    Chapter 2 Constraint-Based Learning Bayesian Networks Using Bayes Factor
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    Chapter 3 Learning Bayesian Network Parameters from Small Data Set: A Spatially Maximum a Posteriori Method
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    Chapter 4 Hashing-Based Hybrid Duplicate Detection for Bayesian Network Structure Learning
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    Chapter 5 A Bayesian Network Approach for Predicting Purchase Behavior via Direct Observation of In-store Behavior
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    Chapter 6 Statistical Tests for Joint Analysis of Performance Measures
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    Chapter 7 Extending Naive Bayes Classifier with Hierarchy Feature Level Information for Record Linkage
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    Chapter 8 Empirical Behavior of Bayesian Network Structure Learning Algorithms
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    Chapter 9 On Model Selection, Bayesian Networks, and the Fisher Information Integral
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    Chapter 10 Unsupervised Evolutionary Algorithm for Dynamic Bayesian Network Structure Learning
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    Chapter 11 A Fast Clique Maintenance Algorithm for Optimal Triangulation of Bayesian Networks
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    Chapter 12 Factorization of ZDDs for Representing Bayesian Networks Based on d -Separations
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    Chapter 13 Missing Data from a Causal Perspective
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    Chapter 14 Learning Maximal Ancestral Graphs with Robustness for Faithfulness Violations
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    Chapter 15 Discriminative and Generative Models in Causal and Anticausal Settings
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    Chapter 16 A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes
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    Chapter 17 Forest Learning Based on the Chow-Liu Algorithm and Its Application to Genome Differential Analysis: A Novel Mutual Information Estimation
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    Chapter 18 Tips and Tricks for Building Bayesian Networks for Scoring Game-Based Assessments
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Mentioned by

twitter
1 X user

Citations

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5 Dimensions

Readers on

mendeley
43 Mendeley
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Title
Advanced Methodologies for Bayesian Networks
Published by
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-28379-1
ISBNs
978-3-31-928378-4, 978-3-31-928379-1
Editors

Joe Suzuki, Maomi Ueno

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Malaysia 1 2%
United States 1 2%
Netherlands 1 2%
Unknown 39 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 30%
Student > Master 10 23%
Researcher 6 14%
Student > Bachelor 4 9%
Student > Doctoral Student 3 7%
Other 4 9%
Unknown 3 7%
Readers by discipline Count As %
Engineering 12 28%
Computer Science 10 23%
Social Sciences 4 9%
Environmental Science 4 9%
Agricultural and Biological Sciences 2 5%
Other 7 16%
Unknown 4 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 January 2016.
All research outputs
#15,356,841
of 22,844,985 outputs
Outputs from Lecture notes in computer science
#4,648
of 8,126 outputs
Outputs of similar age
#209,075
of 353,225 outputs
Outputs of similar age from Lecture notes in computer science
#164
of 257 outputs
Altmetric has tracked 22,844,985 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,225 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 257 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.