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A multidimensional unfolding method based on Bayes' theorem
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Cited by
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ALICE: Physics Performance Report, Volume II
Collaboration, A., Alessandro, B., Antinori, F., Belikov, J. A., Blume, C., Dainese, A., Foka, P., Giubellino, P., Hippolyte, B., Kuhn, C., et al. (2006) · Journal of Physics G: Nuclear and Particle Physics · doi:10.1088/0954-3899/32/10/001 · Cited by 549
THE FIRST FERMI LARGE AREA TELESCOPE CATALOG OF GAMMA-RAY PULSARS
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Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020) · Electronics · doi:10.3390/electronics9071177 · Cited by 378
Metadata
- Authors
- D'Agostini, G.
- Venue
- Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
- Publisher
- Elsevier BV
- Published
- 1995-08
- Volume
- 362
- Issue
- 2-3
- Pages
- 487-498
- ISSN
- 0168-9002 (ISSN-L)
- OpenAlex
- W2169733482
- Cited by
- 1,267
- References
- 0
- FWCI
- 8.68
- Type
- journal article
- Language
- en
- Open access
- closed
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How to cite
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- CoChat. (2026). Citation check: A multidimensional unfolding method based on Bayes' theorem. https://app.cochat.ai/papers/10.1016/0168-9002(95)00274-x
- The paper
- D'Agostini, G. (1995). A multidimensional unfolding method based on Bayes' theorem. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. https://doi.org/10.1016/0168-9002(95)00274-x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
