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Cited by 54Markov Chains and Monte Carlo MethodsStatistical Methods and Bayesian InferenceGaussian Processes and Bayesian Inference

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Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter

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Mbalawata, Isambi S., Särkkä, Simo, Vihola, Matti, Haario, Heikki·2015·Computational Statistics & Data Analysis
DOI 10.1016/j.csda.2014.10.006
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Research works printed but not linked: 27

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Metadata

Authors
Mbalawata, I. S., Särkkä, S., Vihola, M., & Haario, H.
Affiliations
  • Lappeenranta-Lahti University of Technology (FI)
  • Aalto University (FI)
  • University of Oxford (GB)
Corresponding author
Mbalawata, Isambi S.
ORCID
Venue
Computational Statistics & Data Analysis
Publisher
Elsevier BV
Published
2015-03
Volume
83
Pages
101-115
ISSN
0167-9473 (ISSN-L)
DOI
10.1016/j.csda.2014.10.006
OpenAlex
W1976905360
Cited by
54
References
1
Citations by year
2025: 5 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 8 · 2020: 2 · 2019: 4 · 2018: 8 · 2017: 6 · 2016: 6 · 2015: 3
FWCI
5.29
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Computational Statistics & Data Analysis (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • Finnish Centre of Excellence on Inverse Problems Research of Academy of Finland
  • Academy of Finland
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Keywords
adaptive Metropolis algorithm, adaptive MCMC, Markov chain Monte Carlo, strong law of large numbers

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