Divide-and-conquer Metropolis–Hastings samplers with matched samples
Author:
Affiliation:
1. The MathWorks, Inc. 55 Centre Street, Natick, Massachusetts 01760, USA
Publisher
Institute of Mathematical Statistics
Subject
Statistics and Probability
Reference20 articles.
1. Chopin, N. (2002). A sequential particle filter method for static models. Biometrika 89, 539–551.
2. Scott, S. L., Blocker, A. W., Bonassi, F. V., Chipman, H. A., George, E. I. and McCulloch, R. E. (2016). Bayes and big data: The consensus Monte Carlo algorithm. International Journal of Management Science and Engineering Management 11, 78–88.
3. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087–1092.
4. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97–109.
5. Banterle, M., Crazian, C., Lee, A. and Robert, C. (2019). Accelerating Metropolis–Hastings algorithms by delayed acceptance. Foundations of Data Science 1, 103–128.
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