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Cited by 77Markov Chains and Monte Carlo MethodsTarget Tracking and Data Fusion in Sensor NetworksControl Systems and Identification

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Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering

Mbalawata, Isambi S., Särkkä, Simo, Haario, Heikki·2013·Computational Statistics
DOI 10.1007/s00180-012-0352-y
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Metadata

Authors
Mbalawata, I. S., Särkkä, S., & Haario, H.
Affiliations
  • Lappeenranta-Lahti University of Technology (FI)
  • Aalto University (FI)
Corresponding author
Mbalawata, Isambi S.
ORCID
Venue
Computational Statistics
Publisher
Springer Science and Business Media LLC
Published
2012-08-03
Volume
28
Issue
3
Pages
1195-1223
ISSN
  • 0943-4062 (ISSN-L)
  • 1613-9658
DOI
10.1007/s00180-012-0352-y
OpenAlex
W2060182299
Cited by
77
References
0
Citations by year
2026: 2 · 2025: 5 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 5 · 2020: 6 · 2019: 21 · 2018: 5 · 2017: 6 · 2016: 3 · 2015: 5 · 2014: 6 · 2013: 6
FWCI
5.20
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
  • Computational Statistics (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • Academy of Finland
  • Aalto-Yliopisto
  • Lappeenranta University of Technology
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
  • Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords
parameter estimation, stochastic differential equations, Itô processes, Markov chain Monte Carlo, Metropolis-Hastings algorithm, Hamiltonian Monte Carlo, gradient computation, linear stochastic differential equations

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