Smoothing Estimation

Smoothing Estimation is a statistical estimation method that belongs to the class of sequential Bayesian inference techniques, distinguished by its use of future and past observations to refine the estimate of a hidden state at a given time, thereby reducing estimation error in dynamic systems.

169

Publications

12.5K

Citations

341

Authors

242

Institutions

Publications per year

2017–2026

16

Authors

341

  1. TK
    Toshihiko Kotake

    Osaka International Cancer Institute, Japan

    3 publications · 158 citations

    3

    h-index

  2. TK
    T. Kailath

    Stanford University, United States

    3 publications · 420 citations

    3

    h-index

  3. SH
    Shiro Hinotsu

    University of Tsukuba, Japan

    3 publications · 158 citations

    3

    h-index

  4. AD
    Arnaud Doucet

    University of Cambridge, United Kingdom

    3 publications · 201 citations

    3

    h-index

  5. HA
    Hideyuki Akaza

    University of Tsukuba, Japan

    3 publications · 158 citations

    3

    h-index

1–5 of 341

Venues

  1. IEEE Transactions on Automatic Control

    14 publications · 894 citations

    13

    h-index

  2. Biometrika

    6 publications · 325 citations

    6

    h-index

  3. The Annals of Statistics

    5 publications · 280 citations

    5

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 4 publications · 170 citations

    4

    h-index

  5. Statistics & Probability Letters

    3 publications · 65 citations

    3

    h-index

1–5