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Award ID contains: 1811812

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  1. ; ; (, Journal of the American Statistical Association)
    Full Text Available 
  2. ; ; (, Journal of Statistical Computation and Simulation)
    Full Text Available 
  3. ; ; (, Journal of Risk and Insurance)
  4. ; ; (, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))
    Accepted Manuscript Full Text Available
  5. ; ; (, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, Biometrika)
    Summary Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms. 
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  7. ; (, Sankhya A)
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  8. (, Electronic Journal of Statistics)
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  9. ; ; ; ; ; (, IEEE Transactions on Pattern Analysis and Machine Intelligence)
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  10. ; ; (, Advances in Neural Information Processing Systems 33 (NeurIPS 2020))
    Accepted Manuscript Full Text Available