I. R. Goodman

1.4k total citations
47 papers, 791 citations indexed

About

I. R. Goodman is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computational Theory and Mathematics. According to data from OpenAlex, I. R. Goodman has authored 47 papers receiving a total of 791 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 16 papers in Management Science and Operations Research and 12 papers in Computational Theory and Mathematics. Recurrent topics in I. R. Goodman's work include Bayesian Modeling and Causal Inference (24 papers), Logic, Reasoning, and Knowledge (18 papers) and Multi-Criteria Decision Making (14 papers). I. R. Goodman is often cited by papers focused on Bayesian Modeling and Causal Inference (24 papers), Logic, Reasoning, and Knowledge (18 papers) and Multi-Criteria Decision Making (14 papers). I. R. Goodman collaborates with scholars based in United States and Mexico. I. R. Goodman's co-authors include Hung T. Nguyen, Ronald Mahler, Hung T. Nguyen, Samuel Kotz, Donald Bamber, Gerald S. Rogers, Владик Крейнович, Theodore V. Galambos, JingTao Yao and Lotfi Zadeh and has published in prestigious journals such as Information Sciences, Fuzzy Sets and Systems and Journal of Mathematical Analysis and Applications.

In The Last Decade

I. R. Goodman

41 papers receiving 713 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
I. R. Goodman United States	10	606	202	157	128	120	47	791
John T. Rickard United States	11	347 0.6×	177 0.9×	60 0.4×	86 0.7×	102 0.8×	39	695
Alessio Benavoli Switzerland	15	491 0.8×	76 0.4×	156 1.0×	67 0.5×	78 0.7×	72	754
Tim Zajic United States	12	284 0.5×	196 1.0×	231 1.5×	26 0.2×	144 1.2×	32	714
Vladislav B. Tadić United Kingdom	12	484 0.8×	66 0.3×	108 0.7×	54 0.4×	109 0.9×	43	784
J.S. Sadowsky United States	16	197 0.3×	436 2.2×	341 2.2×	40 0.3×	263 2.2×	60	1.1k
Hossein Tahani United States	7	458 0.8×	163 0.8×	22 0.1×	110 0.9×	112 0.9×	9	685
K. Mehrotra United States	15	585 1.0×	38 0.2×	178 1.1×	46 0.4×	115 1.0×	41	1.0k
Michael G. Thomason United States	16	334 0.6×	152 0.8×	324 2.1×	164 1.3×	49 0.4×	53	1.0k
Javier Yáñez Spain	16	180 0.3×	192 1.0×	49 0.3×	135 1.1×	50 0.4×	48	607
Marie-Hélène Masson France	17	786 1.3×	309 1.5×	39 0.2×	262 2.0×	205 1.7×	41	1.2k

Countries citing papers authored by I. R. Goodman

Since Specialization

Citations

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

Fields of papers citing papers by I. R. Goodman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I. R. Goodman. 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 I. R. Goodman. The network helps show where I. R. Goodman may publish in the future.

Co-authorship network of co-authors of I. R. Goodman

This figure shows the co-authorship network connecting the top 25 collaborators of I. R. Goodman. A scholar is included among the top collaborators of I. R. Goodman 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 I. R. Goodman. I. R. Goodman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Bamber, Donald, I. R. Goodman, Arjun K. Gupta, & Hung T. Nguyen. (2010). Use of the global implicit function theorem to induce singular conditional distributions on surfaces in n dimensions: Part I. Random Operators and Stochastic Equations. 18(4). 355–389.

Bamber, Donald, I. R. Goodman, & Hung T. Nguyen. (2004). Deduction from conditional knowledge. Soft Computing. 8(4). 247–255. 5 indexed citations

Bamber, Donald, et al.. (2003). A new method for representing linguistic quantifications by random sets with applications to tracking and data fusion. 2. 1308–1315. 3 indexed citations

Goodman, I. R., et al.. (2002). New applications of relational event algebra to fuzzy quantification and probabilistic reasoning. Information Sciences. 148(1-4). 87–96. 3 indexed citations

Крейнович, Владик, et al.. (2002). A realistic (non-associative) logic and a possible explanations of 7±2 law. International Journal of Approximate Reasoning. 29(3). 235–266. 9 indexed citations

Bamber, Donald & I. R. Goodman. (2001). Reasoning with Assertions of High Conditional Probability: Entailment with Universal Near Surety.. 17–26. 2 indexed citations

Bamber, Donald, et al.. (2001). <title>Complexity reducing algorithm for near optimal fusion (CRANOF) with application to tracking and information fusion</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4380. 269–280. 4 indexed citations

Bamber, Donald, et al.. (2001). Extension of the concept of propositional deduction from classical logic to probability: an overview of probability-selection approaches. Information Sciences. 131(1-4). 195–250. 6 indexed citations

Goodman, I. R.. (1998). Application of Frechet and other random-set averaging techniques to fusion of information. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3374. 108–108. 1 indexed citations

10.

Nguyen, Hung T. & I. R. Goodman. (1994). On modeling of if-then rules for probabilistic inference. International Journal of Intelligent Systems. 9(4). 411–418. 4 indexed citations

11.

Goodman, I. R.. (1994). A New Characterization of Fuzzy Logic Operators Producing Homomorphic-Like Relations with One-Point Coverages of Random Sets.. 8 indexed citations

12.

Goodman, I. R. & Hung T. Nguyen. (1994). A theory of conditional information for probabilistic inference in intelligent systems: II. Product space approach. Information Sciences. 76(1-2). 13–42. 12 indexed citations

13.

Goodman, I. R.. (1992). A Revised Approach to Combining Linguistic and Probabilistic Information in Correlation. Defense Technical Information Center (DTIC). 1 indexed citations

14.

Goodman, I. R., Hung T. Nguyen, & Gerald S. Rogers. (1991). On the scoring approach to admissibility of uncertainty measures in expert systems. Journal of Mathematical Analysis and Applications. 159(2). 550–594. 11 indexed citations

15.

Goodman, I. R.. (1991). Evaluation of combinations of conditioned information: A history. Information Sciences. 57-58. 79–110. 5 indexed citations

16.

Goodman, I. R., et al.. (1989). Extension of the Measure-Free Approach to Conditioning of Fuzzy Sets and Other Logics.. 1 indexed citations

17.

Yao, JingTao, JingTao Yao, Theodore V. Galambos, et al.. (1983). NAFIP-1. Panel discussion on introduction of fuzzy sets to undergraduate engineering and science curricula. International Journal of Man-Machine Studies. 19(1). 5–7. 2 indexed citations

18.

Goodman, I. R.. (1982). PACT: Possibilistic Approach to Correlation and Tracking. Defense Technical Information Center (DTIC). 1 indexed citations

19.

Goodman, I. R.. (1979). A general model for the multiple target correlation and tracking problem. 383–388. 3 indexed citations

20.

Goodman, I. R. & Samuel Kotz. (1973). Multivariate θ-generalized normal distributions. Journal of Multivariate Analysis. 3(2). 204–219. 42 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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