Identifiability Criteria

Identifiability Criteria is a formal framework within mathematical and statistical modeling that specifies the necessary and sufficient conditions under which the parameters of a model can be uniquely determined from observable data.

320

Publications

20.7K

Citations

645

Authors

360

Institutions

Publications per year

2017–2026

54

Authors

645

  1. GB
    Georgios B. Giannakis

    University of Minnesota, United States

    6 publications · 1.4K citations

    6

    h-index

  2. ET
    Edison Tse

    Stanford University, United States

    5 publications · 199 citations

    5

    h-index

  3. ND
    Nicholas D. Sidiropoulos

    University of Minnesota, United States

    5 publications · 895 citations

    5

    h-index

  4. ÉW
    Éric Walter

    Pacific Northwest National Laboratory, United States

    5 publications · 317 citations

    5

    h-index

  5. MP
    Maria Pia Saccomani

    University of Padua, Italy

    5 publications · 112 citations

    5

    h-index

1–5 of 645

Venues

  1. IEEE Transactions on Signal Processing

    16 publications · 2K citations

    13

    h-index

  2. IEEE Transactions on Automatic Control

    13 publications · 564 citations

    13

    h-index

  3. Mathematical Biosciences

    12 publications · 1.1K citations

    11

    h-index

  4. IFAC Proceedings Volumes

    9 publications · 284 citations

    9

    h-index

  5. arXiv (Cornell University)

    Repository · 9 publications · 403 citations

    9

    h-index

1–5