Sample-based Inference

Sample-Based Inference is a methodological approach within inductive reasoning that involves drawing general conclusions or constructing predictive models about a population based on the analysis of a subset of observed instances, distinguished by its reliance on empirical data samples to approximate broader patterns or principles.

120

Publications

47K

Citations

234

Authors

136

Institutions

Publications per year

2017–2026

25

Authors

234

  1. GJ
    Gaofeng Jia

    Colorado State University, United States

    4 publications · 86 citations

    4

    h-index

  2. PD
    Peter Dayan

    University College London, United Kingdom

    3 publications · 218 citations

    3

    h-index

  3. AR
    Ananth Ranganathan

    Georgia Institute of Technology, United States

    3 publications · 138 citations

    3

    h-index

  4. ST
    Sebastian Thrun

    Carnegie Mellon University, United States

    3 publications · 983 citations

    3

    h-index

  5. DS
    David Silver

    Google DeepMind (United Kingdom), United Kingdom

    3 publications · 218 citations

    3

    h-index

1–5 of 234

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 207 citations

    6

    h-index

  2. eScholarship (California Digital Library)

    Repository · 4 publications · 269 citations

    4

    h-index

  3. Biometrika

    3 publications · 283 citations

    3

    h-index

  4. 3

    h-index

  5. Educational Studies in Mathematics

    2 publications · 110 citations

    2

    h-index

1–5