Jim Q. Smith

2.9k citations
62 papers · 1.7k indexed · 1 hit paper · h-index 17
Topics
Bayesian Modeling and Causal Inference (29 papers)Statistical Methods and Bayesian Inference (6 papers)Statistical Methods and Inference (6 papers)

In The Last Decade

Jim Q. Smith

56 papers receiving 1.6k citations

Hit Papers

Bayesian Statistics 4.19932026200420151993250500750

Peers

Jim Q. Smith
Comparison fields: 5 of 167
  • Artificial Intelligence 570
  • Statistics and Probability 485
  • Molecular Biology 202
  • Management Science and Operations Research 202
  • Genetics 188
Replace Jingchen Liu with:
Jingchen Liu United States
Yogendra P. Chaubey Canada
Yanan Fan Australia
Steven L. Scott United States
Ramalingam Shanmugam United States
Kenneth J. Koehler United States
Campbell B. Read United States
D. J. Best Australia
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Jim Q. Smith relative to Jingchen Liu United States Jingchen Liu's profile →
Citations per field
00.5×3.2×
Jingchen Liu · 1×
Citations per year

Countries citing papers authored by Jim Q. Smith

Since Specialization
Citations

This map shows the geographic impact of Jim Q. Smith'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 Jim Q. Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jim Q. Smith more than expected).

Fields of papers citing papers by Jim Q. Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jim Q. Smith. 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 Jim Q. Smith. The network helps show where Jim Q. Smith may publish in the future.

Co-authorship network of co-authors of Jim Q. Smith

This figure shows the co-authorship network connecting the top 25 collaborators of Jim Q. Smith. A scholar is included among the top collaborators of Jim Q. Smith based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jim Q. Smith. Jim Q. Smith is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 3
4 6
5 25
6 11
7 18
8
Uncertainty handling during nuclear accidents.
2
9 4
10
A Differential Approach to Causality in Staged Trees
1
11 2
12 13
13 34
14 97
15 58
16
Local robustness of Bayesian parametric inference and observed likelihoods
3
17 6
18
On the geometry of Bayesian graphical models with hidden variables
20
19 1
20 3

About Jim Q. Smith

Jim Q. Smith is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 62 papers that have together received 1.7k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (29 papers), Statistical Methods and Bayesian Inference (6 papers) and Statistical Methods and Inference (6 papers). The work is most often cited by research in Statistics and Probability (485 citations), Statistics, Probability and Uncertainty (137 citations) and Artificial Intelligence (570 citations). Jim Q. Smith has collaborated with scholars based in United Kingdom, United States and Italy. Frequent co-authors include A. F. M. Smith, James O. Berger, A. P. Dawid, Christian P. Robert, Paul E. Anderson, Bela Sharma, Thomas Hill, Silvia Liverani, Eva Riccomagno and Raffaella Settimi. Their work appears in journals such as Journal of the American Statistical Association, NeuroImage and Management Science.

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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