Iterative Hypothesis Refinement

Iterative Hypothesis Refinement is a methodological process in which a hypothesis is systematically modified and improved through successive cycles of testing, evaluation, and adjustment to enhance its explanatory or predictive validity within a given domain.

23

Publications

3.7K

Citations

99

Authors

47

Institutions

Publications per year

2017–2026

8

Authors

99

  1. MA
    Milad Abolhasani

    North Carolina State University, United States

    2 publications · 65 citations

    2

    h-index

  2. FD
    Fernando Delgado‐Licona

    North Carolina State University, United States

    2 publications · 65 citations

    2

    h-index

  3. LL
    Linjie Li

    Microsoft Research (United Kingdom), United Kingdom

    2 publications · 134 citations

    2

    h-index

  4. YC
    Yu Cheng

    Microsoft (United States), United States

    2 publications · 134 citations

    2

    h-index

  5. JL
    Jingjing Liu

    Microsoft (United States), United States

    2 publications · 134 citations

    2

    h-index

1–5 of 99

Venues

  1. Advanced Intelligent Systems

    2 publications · 65 citations

    2

    h-index

  2. Technometrics

    2 publications · 149 citations

    2

    h-index

  3. Trials

    1 publications · 27 citations

    1

    h-index

  4. Epidemiology and Infection

    1 publications · 27 citations

    1

    h-index

  5. iScience

    1 publications · 15 citations

    1

    h-index

1–5