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
308
On covariance estimation of non-synchronously observed diffusion processes
Takaki Hayashi, Nakahiro Yoshida · Bernoulli · 2005 · 425 citations · Full text
Improved Denoising Diffusion Probabilistic Models
Alex Nichol, Prafulla Dhariwal · arXiv (Cornell University) · 2021 · 412 citations · Full text
Alexandros Beskos, Omiros Papaspiliopoulos, Gareth O. Roberts et al. · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2006 · 356 citations · Full text
1–5 of 308
527
11
h-index
10
h-index
9
h-index
8
h-index
7
h-index
1–5 of 527
310

University of Warwick
Coventry, United Kingdom
13 h-index
23 publications
1.4K citations

Centre National de la Recherche Scientifique
Paris, France
11 h-index
16 publications
426 citations
Université Paris Cité
Paris, France
11 h-index
15 publications
498 citations
University of Copenhagen
Copenhagen, Denmark
10 h-index
14 publications
1.2K citations
The University of Osaka
Osaka, Japan
10 h-index
14 publications
402 citations

University of Amsterdam
Amsterdam, The Netherlands
9 h-index
13 publications
894 citations
The University of Tokyo
Tokyo, Japan
8 h-index
8 publications
634 citations

Universidad de Granada
Granada, Spain
8 h-index
21 publications
524 citations
1–8 of 310
Repository · 24 publications · 1.1K citations
15
h-index
15 publications · 1.7K citations
15
h-index
9 publications · 368 citations
10
h-index
CSDA · 9 publications · 336 citations
9
h-index
9 publications · 483 citations
9
h-index
1–5