Generic Identifiability

Generic Identifiability is a type of model identifiability in which the parameters of a statistical or mathematical model are uniquely determined by the observed data distribution for almost all parameter values, except possibly on a set of Lebesgue measure zero within the parameter space.

23

Publications

1.6K

Citations

52

Authors

37

Institutions

Publications per year

2017–2026

9

Authors

52

  1. SS
    Shengling Shi

    Eindhoven University of Technology, Netherlands

    4 publications · 72 citations

    4

    h-index

  2. XC
    Xiaodong Cheng

    University of Groningen, Netherlands

    4 publications · 72 citations

    4

    h-index

  3. PM
    Paul M.J. Van den Hof

    Delft University of Technology, Netherlands

    4 publications · 72 citations

    4

    h-index

  4. MD
    Mathias Drton

    University of Washington, United States

    2 publications · 25 citations

    2

    h-index

  5. JA
    John A. Rhodes

    University of Alaska Fairbanks, United States

    2 publications · 516 citations

    2

    h-index

1–5 of 52

Venues

  1. IEEE Transactions on Automatic Control

    3 publications · 287 citations

    3

    h-index

  2. 2

    h-index

  3. The Annals of Statistics

    2 publications · 494 citations

    2

    h-index

  4. TU/e Research Portal (Eindhoven University of Technology)

    Repository · 1 publications · 34 citations

    1

    h-index

  5. Linear and Multilinear Algebra

    1 publications · 12 citations

    1

    h-index

1–5