Point Process Filtering

Point Process Filtering is a statistical inference method that belongs to the class of filtering techniques for stochastic processes, specifically distinguished by its application to estimate the latent intensity or state of a point process given observed event times up to the current time.

50

Publications

2.8K

Citations

115

Authors

69

Institutions

Publications per year

2017–2026

10

Authors

115

  1. UT
    Uri T. Eden

    Boston University, United States

    5 publications · 502 citations

    5

    h-index

  2. EN
    Emery N. Brown

    Massachusetts General Hospital, United States

    5 publications · 668 citations

    5

    h-index

  3. LM
    Loren M. Frank

    University of California, San Francisco, United States

    4 publications · 580 citations

    4

    h-index

  4. RB
    Riccardo Barbieri

    Massachusetts General Hospital, United States

    4 publications · 584 citations

    4

    h-index

  5. MM
    Maryam M. Shanechi

    University of Southern California, United States

    3 publications · 187 citations

    3

    h-index

1–5 of 115

Venues

  1. IEEE Transactions on Automatic Control

    4 publications · 126 citations

    4

    h-index

  2. IEEE Transactions on Biomedical Engineering

    3 publications · 227 citations

    3

    h-index

  3. Neural Computation

    3 publications · 459 citations

    3

    h-index

  4. Advances in Applied Probability

    AAP · 2 publications · 155 citations

    2

    h-index

  5. Applied Optics

    2 publications · 40 citations

    2

    h-index

1–5