References
- Bernton, E., Yang, S., Chen, Y., Shephard, N., and Liu, J. S. (2015), “Locally Weighted Markov Chain Monte Carlo,” arXiv preprint arXiv:1506.08852.
- Calderhead, B. (2014), “A General Construction for Parallelizing Metropolis-Hastings Algorithms,” Proceedings of the National Academy of Sciences, 111, 17408–17413.
- Fang, K. T., Liu, M. Q., Qin, H., and Zhou, Y. D. (2018), Theory and Application of Uniform Experimental Designs, Singapore: Springer.
- Hlawka, E. (1961), “Funktionen von beschr a¨ nkter variatiou in der theorie der gleichverteilung,” Annali Di Matematica Pura Ed Applicata, 54, 325–333.
- Hobson, M. P., and McLachlan, C. (2003), “A Bayesian Approach to Discrete Object Detection in Astronomical Data Sets,” Monthly Notices of the Royal Astronomical Society, 338, 765–784.
- L’Ecuyer, P. (2016), “Randomized Quasi-Monte Carlo: An Introduction for Practitioners,” in International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Cham: Springer, pp. 29–52.
- Lemieux, C. (2009), Monte Carlo and Quasi-Monte Carlo Sampling, New York: Springer.
- Liang, F., and Wong, W. H. (2001), “Real-Parameter Evolutionary Monte Carlo with applications to Bayesian Mixture Models,” Journal of the American Statistical Association, 96, 653–666.
- Liu, J. S., Liang, F., and Wong, W. H. (2000), “The Multiple-try Method and Local Optimization in Metropolis Sampling,” Journal of the American Statistical Association, 95, 121–134.
- Llorente, F., Martino, L., Delgado-Gómez, D., and Camps-Valls, G. (2021), “Deep Importance Sampling based on Regression for Model Inversion and Emulation,” Digital Signal Processing, 116, 103104.
- Martino, L. (2018), “A Review of Multiple Try MCMC Algorithms for Signal Processing,” Digital Signal Processing, 75, 134–152.
- Miasojedow, B., Moulines, E., and Vihola, M. (2013), “An Adaptive Parallel Tempering Algorithm,” Journal of Computational and Graphical Statistics, 22, 649–664.
- Ning, J. H., and Tao, H. Q. (2020), “Randomized Quasi-Random Sampling/Importance Resampling,” Communications in Statistics-Simulation and Computation, 49, 3367–3379.
- Owen, A. B., and Tribble, S. D. (2005), “A quasi-Monte Carlo Metropolis Algorithm,” Proceedings of the National Academy of Sciences, 102, 8844–8849.
- Pompe, E., Holmes, C., and Łatuszyński, K. (2020), “A Framework for Adaptive MCMC Targeting Multimodal Distributions,” The Annals of Statistics, 48, 2930–2952.
- Robert, C. P., and Casella, G. (2013), Monte Carlo Statistical Methods, New York: Springer.
- Schwedes, T., and Calderhead, B. (2018), “Quasi Markov Chain Monte Carlo Methods,” arXiv preprint arXiv:1807.00070.
- Skare, Ø., Bølviken, E., and Holden, L. (2003), “Improved Sampling-Importance Resampling and Reduced Bias Importance Sampling,” Scandinavian Journal of Statistics, 30, 719–737.
- Tjelmeland, H., and Hegstad, B. K. (2001), “Mode Jumping Proposals in MCMC,” Scandinavian Journal of Statistics, 28, 205–223.
- Vandevoestyne, B., and Cools, R. (2010), “On the Convergence of Quasi-Random Sampling/Importance Resampling,” Mathematics and Computers in Simulation, 81, 490–505.
- Wang, Y. C., Ning, J. H., Zhou, Y. D., and Fang, K. T. (2015), “A New Sampler: Randomized Likelihood Sampling,” in Souvenir Booklet of the 24th International Workshop on Matrices and Statistics, pp. 255–261.