Bayesian System Identification
Bayesian System Identification is a probabilistic framework within system identification that uses Bayesian inference to estimate the posterior distribution of model parameters, incorporating prior knowledge and observed data to quantify uncertainty in dynamical system models.
255
Publications
13.5K
Citations
614
Authors
293
Institutions
Publications per year
2017–2026
109
255
1–5 of 255
614
15
h-index
11
h-index
7
h-index
Hong Kong University of Science and Technology, Hong Kong SAR China
6 publications · 208 citations
6
h-index
6
h-index
1–5 of 614
293

City University of Hong Kong
Hong Kong, Hong Kong
14 h-index
24 publications
1.8K citations
California Institute of Technology
Pasadena, United States
12 h-index
20 publications
4K citations

Harbin Institute of Technology
Harbin, China
9 h-index
17 publications
1.2K citations

University of Alberta
Edmonton, Canada
8 h-index
21 publications
722 citations

University of Padua
Padua, Italy
8 h-index
13 publications
485 citations
University of Sheffield
Sheffield, United Kingdom
8 h-index
18 publications
883 citations
Tongji University
Shanghai, China
8 h-index
15 publications
493 citations
Eindhoven University of Technology
Eindhoven, The Netherlands
6 h-index
16 publications
1.1K citations
1–8 of 293
MSSP · 26 publications · 1.5K citations
21
h-index
10 publications · 793 citations
10
h-index
8 publications · 366 citations
8
h-index
7 publications · 1.5K citations
7
h-index
CACAIE · 6 publications · 492 citations
6
h-index
1–5