Song, Yang; Ermon, Stefano (2019-12-08), "Generative modeling by estimating gradients of the data distribution", Proceedings of the 33rd International Conference on Neural Information Processing Systems, no. 1067, Red Hook, NY, USA: Curran Associates Inc., pp. 11918–11930, retrieved 2025-04-28
Robert, Christian; Casella, George (2011). "A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data". Statistical Science. 26 (1): 102–115. arXiv:0808.2902. doi:10.1214/10-STS351.
Papaspiliopoulos, Omiros; Roberts, Gareth O.; Sköld, Martin (2007). "A general framework for the parametrization of hierarchical models". Statistical Science. 22 (1). Institute of Mathematical Statistics: 59–73. arXiv:0708.3797. doi:10.1214/088342307000000014.
Óli Páll Geirsson, Birgir Hrafnkelsson, and Helgi Sigurðarson (2015). "A Block Gibbs Sampling Scheme for Latent Gaussian Models." arXiv preprint [arXiv:1506.06285](https://arxiv.org/abs/1506.06285).
Phan, Du; Pradhan, Neeraj; Jankowiak, Martin (2019-12-24). "Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro". arXiv:1912.11554 [stat.ML].
cmu.edu
iiif.library.cmu.edu
Papageorgiou, Anargyros; Traub, Joseph (1996). "Beating Monte Carlo"(PDF). Risk. 9 (6): 63–65.
Robert, Christian; Casella, George (2011). "A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data". Statistical Science. 26 (1): 102–115. arXiv:0808.2902. doi:10.1214/10-STS351.
Papaspiliopoulos, Omiros; Roberts, Gareth O.; Sköld, Martin (2007). "A general framework for the parametrization of hierarchical models". Statistical Science. 22 (1). Institute of Mathematical Statistics: 59–73. arXiv:0708.3797. doi:10.1214/088342307000000014.
Siddhartha Chib and Srikanth Ramamurthy (2009). "Tailored Randomized Block MCMC Methods with Application to DSGE Models." *Journal of Econometrics*, 155(1), 19–38. doi:10.1016/j.jeconom.2009.08.003
Piero Barone, Giovanni Sebastiani, and Jonathan Stander (2002). "Over-relaxation methods and coupled Markov chains for Monte Carlo simulation." Statistics and Computing, 12(1), 17–26. doi:10.1023/A:1013112103963
Gilks, W. R.; Wild, P. (1992-01-01). "Adaptive Rejection Sampling for Gibbs Sampling". Journal of the Royal Statistical Society. Series C (Applied Statistics). 41 (2): 337–348. doi:10.2307/2347565. JSTOR2347565.
Gilks, W. R.; Best, N. G.; Tan, K. K. C. (1995-01-01). "Adaptive Rejection Metropolis Sampling within Gibbs Sampling". Journal of the Royal Statistical Society. Series C (Applied Statistics). 44 (4): 455–472. doi:10.2307/2986138. JSTOR2986138.
Del Moral, Pierre; Miclo, Laurent (2000). "Branching and Interacting Particle Systems Approximations of Feynman-Kac Formulae with Applications to Non-Linear Filtering". In Jacques Azéma; Michel Ledoux; Michel Émery; Marc Yor (eds.). Séminaire de Probabilités XXXIV(PDF). Lecture Notes in Mathematics. Vol. 1729. pp. 1–145. doi:10.1007/bfb0103798. ISBN978-3-540-67314-9.
Sobol, Ilya M (1998). "On quasi-monte carlo integrations". Mathematics and Computers in Simulation. 47 (2): 103–112. doi:10.1016/s0378-4754(98)00096-2.
L'Ecuyer, P.; Munger, D.; Lécot, C.; Tuffin, B. (2018). "Sorting Methods and Convergence Rates for Array-RQMC: Some Empirical Comparisons". Mathematics and Computers in Simulation. 143: 191–201. doi:10.1016/j.matcom.2016.07.010.
Cowles, M.K.; Carlin, B.P. (1996). "Markov chain Monte Carlo convergence diagnostics: a comparative review". Journal of the American Statistical Association. 91 (434): 883–904. CiteSeerX10.1.1.53.3445. doi:10.1080/01621459.1996.10476956.
Gilks, W. R.; Wild, P. (1992-01-01). "Adaptive Rejection Sampling for Gibbs Sampling". Journal of the Royal Statistical Society. Series C (Applied Statistics). 41 (2): 337–348. doi:10.2307/2347565. JSTOR2347565.
Gilks, W. R.; Best, N. G.; Tan, K. K. C. (1995-01-01). "Adaptive Rejection Metropolis Sampling within Gibbs Sampling". Journal of the Royal Statistical Society. Series C (Applied Statistics). 44 (4): 455–472. doi:10.2307/2986138. JSTOR2986138.
Del Moral, Pierre; Miclo, Laurent (2000). "Branching and Interacting Particle Systems Approximations of Feynman-Kac Formulae with Applications to Non-Linear Filtering". In Jacques Azéma; Michel Ledoux; Michel Émery; Marc Yor (eds.). Séminaire de Probabilités XXXIV(PDF). Lecture Notes in Mathematics. Vol. 1729. pp. 1–145. doi:10.1007/bfb0103798. ISBN978-3-540-67314-9.
Tribble, Seth D. (2007). Markov chain Monte Carlo algorithms using completely uniformly distributed driving sequences (Diss.). Stanford University. ProQuest304808879.
psu.edu
citeseerx.ist.psu.edu
Cowles, M.K.; Carlin, B.P. (1996). "Markov chain Monte Carlo convergence diagnostics: a comparative review". Journal of the American Statistical Association. 91 (434): 883–904. CiteSeerX10.1.1.53.3445. doi:10.1080/01621459.1996.10476956.