Terrence L. Fine

3.2k total citations · 1 hit paper
69 papers, 1.9k citations indexed

About

Terrence L. Fine is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Terrence L. Fine has authored 69 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 11 papers in Computational Theory and Mathematics and 9 papers in Computer Networks and Communications. Recurrent topics in Terrence L. Fine's work include Neural Networks and Applications (11 papers), Bayesian Modeling and Causal Inference (9 papers) and Machine Learning and Algorithms (6 papers). Terrence L. Fine is often cited by papers focused on Neural Networks and Applications (11 papers), Bayesian Modeling and Causal Inference (9 papers) and Machine Learning and Algorithms (6 papers). Terrence L. Fine collaborates with scholars based in United States, Brazil and Australia. Terrence L. Fine's co-authors include Ian Hacking, Emmanuel J. Candès, Peter Walley, Marepalli B. Rao, Martin Harwit, N. J. A. Sloane, Sayandev Mukherjee, P. I. Fierens, Chin‐Jen Ku and Michael Kaplan and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and IEEE Transactions on Automatic Control.

In The Last Decade

Terrence L. Fine

64 papers receiving 1.6k citations

Hit Papers

The Emergence of Probability. 1978 2026 1994 2010 1978 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The Emergence of Probability.
    1978 603 citations Terrence L. Fine et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Terrence L. Fine United States 19 643 282 251 212 203 69 1.9k
B. C. Brookes United Kingdom 19 614 1.0× 207 0.7× 289 1.2× 169 0.8× 202 1.0× 66 2.7k
S. L. Zabell United States 21 425 0.7× 364 1.3× 174 0.7× 219 1.0× 75 0.4× 59 1.3k
Peter Grünwald Netherlands 21 923 1.4× 329 1.2× 262 1.0× 55 0.3× 156 0.8× 98 1.8k
G. A. Barnard United Kingdom 19 374 0.6× 563 2.0× 230 0.9× 144 0.7× 85 0.4× 57 1.8k
George Pólya United States 19 478 0.7× 587 2.1× 126 0.5× 118 0.6× 1.0k 5.0× 50 5.2k
Bruno de Finetti Italy 16 612 1.0× 446 1.6× 322 1.3× 229 1.1× 132 0.7× 40 1.7k
James Franklin Australia 12 394 0.6× 84 0.3× 98 0.4× 96 0.5× 83 0.4× 88 1.6k
J. Aczél Canada 27 689 1.1× 507 1.8× 1.5k 6.0× 67 0.3× 737 3.6× 130 5.0k
F.W. Roush United States 24 335 0.5× 115 0.4× 592 2.4× 41 0.2× 601 3.0× 160 2.1k
David Cox United Kingdom 18 325 0.5× 942 3.3× 370 1.5× 45 0.2× 98 0.5× 42 2.7k

Countries citing papers authored by Terrence L. Fine

Since Specialization
Citations

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

Fields of papers citing papers by Terrence L. Fine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Terrence L. Fine. 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 Terrence L. Fine. The network helps show where Terrence L. Fine may publish in the future.

Co-authorship network of co-authors of Terrence L. Fine

This figure shows the co-authorship network connecting the top 25 collaborators of Terrence L. Fine. A scholar is included among the top collaborators of Terrence L. Fine 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 Terrence L. Fine. Terrence L. Fine 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
1.
Fine, Terrence L., et al.. (2006). A Bayesian Independence Test for Small Datasets. IEEE Transactions on Signal Processing. 54(10). 4026–4031. 7 indexed citations
2.
Rêgo, Leandro Chaves & Terrence L. Fine. (2005). Estimation of Chaotic Probabilities. 297–305. 2 indexed citations
3.
Fine, Terrence L., et al.. (2004). Optimal Power and Retransmission Control Policies for Random Access Systems. IEEE/ACM Transactions on Networking. 12(6). 1156–1166. 21 indexed citations
4.
Fierens, P. I. & Terrence L. Fine. (2003). Towards a Chaotic Probability Model for Frequentist Probability: The Univariate Case.. 110(3). 245–259. 2 indexed citations
5.
Fine, Terrence L. & P. I. Fierens. (2003). Towards a chaotic probability model for frequentist probability. 4 indexed citations
6.
Fine, Terrence L., Stephen B. Wicker, & Thomas Berger. (2002). Sensor-assisted multiple access protocols for wireless networks. 2. 1149–1153. 1 indexed citations
7.
Fine, Terrence L., Stephen B. Wicker, Thomas Berger, & Joseph Y. Halpern. (2002). Sensor-assisted ALOHA for wireless networks. 161–161. 1 indexed citations
8.
Fine, Terrence L., et al.. (2002). Adaptive blind equalization using artificial neural networks. Proceedings of International Conference on Neural Networks (ICNN'96). 4. 1974–1979. 1 indexed citations
9.
Fierens, P. I. & Terrence L. Fine. (2001). Towards a Frequentist Interpretation of Sets of Measures.. 179–187. 3 indexed citations
10.
Fine, Terrence L., et al.. (1998). Neural-network design for small training sets of high dimension. IEEE Transactions on Neural Networks. 9(2). 266–280. 50 indexed citations
11.
Turmon, M. & Terrence L. Fine. (1994). Sample Size Requirements for Feedforward Neural Networks. Neural Information Processing Systems. 7. 327–334. 6 indexed citations
12.
Mukherjee, Sayandev & Terrence L. Fine. (1994). Asymptotics of Gradient-based Neural Network Training Algorithms. Neural Information Processing Systems. 7. 335–342. 4 indexed citations
13.
Fine, Terrence L., et al.. (1992). Forecasting Demand for Electric Power. Neural Information Processing Systems. 5. 739–746. 14 indexed citations
14.
Fine, Terrence L., et al.. (1991). Stationarity and almost sure divergence of time averages in interval-valued probability. Journal of Theoretical Probability. 4(2). 239–260. 4 indexed citations
15.
Fine, Terrence L., et al.. (1982). Bayes-like Decision Making with Upper and Lower Probabilities. Journal of the American Statistical Association. 77(377). 80–80. 14 indexed citations
16.
Fine, Terrence L., et al.. (1982). Bayes-like Decision Making with Upper and Lower Probabilities. Journal of the American Statistical Association. 77(377). 80–88. 43 indexed citations
17.
Walley, Peter & Terrence L. Fine. (1979). Varieties of modal (classificatory) and comparative probability. Synthese. 41(3). 321–374. 38 indexed citations
18.
Fine, Terrence L.. (1975). Uniformly reasonable source encoding is often practically impossible. IEEE Transactions on Information Theory. 21(4). 368–373. 1 indexed citations
19.
Fine, Terrence L.. (1971). Stochastic Independence and Computational Complexity.. IFIP Congress. 46–51.
20.
Fine, Terrence L.. (1970). Extrapolation when very little is known about the source. Information and Control. 16(4). 331–359. 16 indexed citations

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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