Cited by 123Bayesian Modeling and Causal InferenceExplainable Artificial Intelligence (XAI)Gaussian Processes and Bayesian Inference

Bayesian networks for interpretable machine learning and optimization

Open access
Mihaljević, Bojan, Bielza, Concha, Larrañaga, Pedro·2021·Neurocomputing

Integrity signals

Registry

Crossref · 10.1016/j.neucom.2021.01.138

Cites

164 of 164 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

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.

Cites

Flagged first, then by citations

7 of the 171 references couldn’t be matched to a known paper.

Cited by

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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
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 ›

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