Variational Bayesian analysis of nonhomogeneous hidden Markov models with long and ultralong sequences-Reference-Cited by-同舟云学术

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Variational Bayesian analysis of nonhomogeneous hidden Markov models with long and ultralong sequences

Published:2023-06-01 Issue:2 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Chen Xinyuan¹,Li Yiwei²,Feng Xiangnan³,Chang Joseph T.⁴

Affiliation:

1. Department of Mathematics and Statistics, Mississippi State University

2. Department of Marketing and International Business, Lingnan University

3. Department of Statistics and Data Science, Fudan University

4. Department of Statistics and Data Science, Yale University

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference51 articles.

1. ONG, V. M.-H., NOTT, D. J. and SMITH, M. S. (2018). Gaussian variational approximation with a factor covariance structure. J. Comput. Graph. Statist. 27 465–478.

2. ARNOLD, L. (1998). Random Dynamical Systems. Springer Monographs in Mathematics. Springer, Berlin.

3. FRÜHWIRTH-SCHNATTER, S. (2001). Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models. J. Amer. Statist. Assoc. 96 194–209.

4. ABARBANEL, H. D. I., BROWN, R. and KENNEL, M. B. (1992). Local Lyapunov exponents computed from observed data. J. Nonlinear Sci. 2 343–365.

5. AICHER, C., MA, Y.-A., FOTI, N. J. and FOX, E. B. (2019). Stochastic gradient MCMC for state space models. SIAM J. Math. Data Sci. 1 555–587.

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