Point Process Inference

Point Process Inference is a statistical inference framework that, as a subclass of probabilistic modeling methods, specifically involves estimating the underlying intensity function, structure, or parameters of a point process from observed event data in continuous space or time.

63

Publications

4K

Citations

125

Authors

71

Institutions

Publications per year

2017–2026

11

Authors

125

  1. AF
    Alan F. Karr

    National Institute of Statistical Sciences, United States

    7 publications · 853 citations

    7

    h-index

  2. SR
    Stephen Roberts

    University of Oxford, United Kingdom

    4 publications · 116 citations

    4

    h-index

  3. CL
    Chris Lloyd

    University of Oxford, United Kingdom

    3 publications · 92 citations

    3

    h-index

  4. MA
    Michael A. Osborne

    University of Oxford, United Kingdom

    3 publications · 92 citations

    3

    h-index

  5. TG
    Tom Gunter

    University of Oxford, United Kingdom

    3 publications · 92 citations

    3

    h-index

1–5 of 125

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 140 citations

    5

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  2. 4

    h-index

  3. 4

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  4. Biometrics

    2 publications · 270 citations

    2

    h-index

  5. Advances in Applied Probability

    AAP · 2 publications · 44 citations

    2

    h-index

1–5