Cory J. Butz

2.4k citations
50 papers · 1.0k indexed · h-index 15

Cory J. Butz

44 papers receiving 948 citations

Peers

Cory J. Butz
Comparison fields: 5 of 108
  • Computational Theory and Mathematics 394
  • Information Systems 507
  • Signal Processing 226
  • Artificial Intelligence 562
  • Computer Science Applications 79
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Ivo Düntsch United Kingdom
Yailé Caballero Mota Cuba
Man-Kwan Shan Taiwan
Yün Peng China
Constance Heitmeyer United States
Kaile Su China
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Citations per year

Countries citing papers authored by Cory J. Butz

Since Specialization
Citations

This map shows the geographic impact of Cory J. Butz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Cory J. Butz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cory J. Butz more than expected).

Fields of papers citing papers by Cory J. Butz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cory J. Butz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Cory J. Butz. The network helps show where Cory J. Butz may publish in the future.

Co-authorship network

The 24 scholars most cited alongside Cory J. Butz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Cory J. Butz Line = papers co-authored together Cory J. Butz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20231
2
An Empirical Study of Methods for SPN Learning and Inference
20181
3
Simple Propagation with Arc-Reversal in Bayesian Networks
20181
4 20174
5
Testing Independencies in Bayesian Networks with i-Separation.
20161
6
Bayesian Network Inference with Simple Propagation
20161
7
Relevant Path Separation: A Faster Method for Testing Independencies in Bayesian Networks
20162
8 20153
9 201525
10 20132
11 20116
12 20100
13
A Comparative Study of Variable Elimination and Arc Reversal in Bayesian Network Inference
20093
14 20086
15 200830
16 2007105
17 200647
18 2004368
19 200416
20
A method for implementing a probabilistic model as a relational database
199529

About Cory J. Butz

Cory J. Butz is a scholar working on Management Science and Operations Research, Artificial Intelligence and Signal Processing, having authored 50 papers that have together received 1.0k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (31 papers), Data Quality and Management (15 papers), Data Management and Algorithms (11 papers), AI-based Problem Solving and Planning (9 papers), Rough Sets and Fuzzy Logic (9 papers), Advanced Database Systems and Queries (7 papers), Logic, Reasoning, and Knowledge (6 papers) and Data Mining Algorithms and Applications (4 papers). The work is most often cited by research in Computational Theory and Mathematics (394 citations), Information Systems (507 citations) and Signal Processing (226 citations). Cory J. Butz has collaborated with scholars based in Canada, Denmark and Poland. Frequent co-authors include Hong Yao, Howard J. Hamilton, Pawan Lingras, Hua Song, S. K. M. Wong, Dan Wu, Yang Xiang, Aijun An, Guoyin Wang and Sheela Ramanna. Their work appears in journals such as European Journal of Operational Research, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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