Inference For Diffusion Processes

Inference for Diffusion Processes is a statistical methodology within stochastic differential equations that specifically involves estimating parameters, latent states, or model structure of diffusion processes from discrete or noisy observations, distinguished by its focus on continuous-time Markov processes governed by stochastic dynamics.

308

Publications

15.2K

Citations

527

Authors

310

Institutions

Publications per year

2017–2026

99

Authors

527

  1. GO
    Gareth O. Roberts

    University of Warwick, United Kingdom

    11 publications · 1.2K citations

    11

    h-index

  2. NY
    Nakahiro Yoshida

    The University of Tokyo, Japan

    11 publications · 1.1K citations

    10

    h-index

  3. MS
    Michael Sørensen

    University of Copenhagen, Denmark

    12 publications · 1.5K citations

    9

    h-index

  4. OP
    Omiros Papaspiliopoulos

    Universitat Pompeu Fabra, Spain

    8 publications · 819 citations

    8

    h-index

  5. AB
    Alexandros Beskos

    University College London, United Kingdom

    7 publications · 710 citations

    7

    h-index

1–5 of 527

Venues

  1. arXiv (Cornell University)

    Repository · 24 publications · 1.1K citations

    15

    h-index

  2. Bernoulli

    15 publications · 1.7K citations

    15

    h-index

  3. The Annals of Statistics

    9 publications · 368 citations

    10

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 9 publications · 336 citations

    9

    h-index

  5. Scandinavian Journal of Statistics

    9 publications · 483 citations

    9

    h-index

1–5