The DOI matches its Crossref record; 164 of the 171 references it cites were found in a registry or an index.
Bayesian networks for interpretable machine learning and optimization
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · cited 1×
“…probabilistic reasoning, implemented with BNs, can be at odds with human reasoning under uncertainty, which is plagued with heuristics and biases [35], formal logic is not necessarily a good model of human reasoning either, as it provides domain-independent rules while human reasoning is…”
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Genetic Programming: On the Programming of Computers by Means of Natural Selection
Koza, J. R. (1992) · Medical Entomology and Zoology · Cited by 13,082
(1999) · Cited by 12,854
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Research works printed but not linked: 7
- W. Samek, T. Wiegand, K.-R. Müller, Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models, arXiv preprint arXiv:1708.08296 (2017). · arXiv
- Clancey, 1984, Use of MYCIN’s rules for tutoring, Rule-Based Expert Systems. Addison-Wesley, Reading, 20
- Heckerman, 2000, Dependency networks for inference, collaborative filtering, and data visualization, J. Mach. Learn. Res., 1, 49
- M. Henrion, M.J. Druzdzel, Uncertainty in Artificial Intelligence, Elsevier, 1991, pp. 17–32.
- Good, 1985, Weight of evidence: A brief survey, Bayesian Stat., 2, 249
- H.J. Suermondt, Explanation in Bayesian Belief Networks, Ph.D. thesis, Stanford, 1993.
- R. Etxeberria, P. Larrañaga, Global optimization using Bayesian networks, in: Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99), pp. 151–173.
Cited by
Sorted by citations
Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110786 · Cited by 233
A scoping review of neurodegenerative manifestations in explainable digital phenotyping
Alfalahi, H., Dias, S. B., Khandoker, A. H., Chaudhuri, К. R., & Hadjileontiadis, L. J. (2023) · npj Parkinson s Disease · doi:10.1038/s41531-023-00494-0 · Cited by 58
Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives
Flammini, F., Alcaraz, C., Bellini, E., Marrone, S., López, J., & Bondavalli, A. (2022) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2022.3227113 · Cited by 43
Neuro-Symbolic Explainable Artificial Intelligence Twin for Zero-Touch IoE in Wireless Network
Munir, M. S., Kim, K. T., Adhikary, A., Saad, W., Shetty, S. S., Park, S.-B., & Hong, C. S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3303713 · Cited by 47
Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106492 · Cited by 42
Metadata
- Authors
- Mihaljević, B., Bielza, C., & Larrañaga, P.
- Affiliations
- Universidad Autónoma de Madrid (ES)
- Universidad Politécnica de Madrid (ES)
- Corresponding author
- Mihaljević, Bojan
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 456
- Pages
- 648-665
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3173192496
- Cited by
- 123
- References
- 171
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 31 · 2023: 22 · 2022: 16 · 2021: 1
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- green
- oa.upm.es
- Licence
- Locations
- Neurocomputing (publishedVersion)
- Hispana (submittedVersion, cc-by-nc-nd)
- UPM Digital Archive (Technical University of Madrid) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- Bayesian networks, model interpretability, probabilistic reasoning, joint probability distribution, conditional independence, directed acyclic graphs, estimation of distribution algorithms, heuristic optimization, evolutionary algorithms, anomaly detection, clustering, supervised classification, temporal modeling, bioinformatics applications, neuroscience applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
