Stochastic Gradient MCMC for Nonlinear State Space Models-Reference-Cited by-同舟云学术

Stochastic Gradient MCMC for Nonlinear State Space Models

Published:2023-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

Aicher Christopher¹,Putcha Srshti²,Nemeth Christopher³,Fearnhead Paul³,Fox Emily⁴

Affiliation:

1. Department of Statistics, University of Washington

2. STOR-i Centre for Doctoral Training, Lancaster University

3. Department of Mathematics and Statistics, Lancaster University

4. Departments of Statistics and Computer Science, Stanford University

Publisher

Institute of Mathematical Statistics

Subject

Applied Mathematics,Statistics and Probability

Reference45 articles.

1. Robbins, H. and Monro, S. (1951). “A stochastic approximation method.” The Annals of Mathematical Statistics, 400–407.

2. Welling, M. and Teh, Y. W. (2011). “Bayesian learning via stochastic gradient Langevin dynamics.” In Proceedings of the 28th International Conference on Machine Learning, 681–688.

3. Baker, J., Fearnhead, P., Fox, E. B., and Nemeth, C. (2019). “Control Variates for Stochastic Gradient MCMC.” Statistics and Computing, 29(3): 599–615.

4. Aicher, C., Ma, Y.-A., Foti, N. J., and Fox, E. B. (2019). “Stochastic gradient MCMC for state space models.” SIAM Journal on Mathematics of Data Science, 1(3): 555–587.

5. Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P., and Fox, E. B. (2023). “Supplementary Material for ”Stochastic gradient MCMC for nonlinear state space models.”.” Bayesian Analysis.

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