The Living Thing / Notebooks :

Markov Chain Monte Carlo methods

Usefulness: 🔧
Novelty: 💡
Uncertainty: 🤪 🤪 🤪
Incompleteness: 🚧 🚧 🚧

What we usually desire ergodicity results for. Despite studying within this area, I have nothing to say about MCMC broadly.

Hamiltonian Monte Carlo

An extension using physics.

Handy tricks

Pierre E. Jacob, John O’Leary, Yves F. Atchadé made MCMC estimators without finite-time-bias, which is nice for parallelisation. (JaOA17_)

Refs

Betancourt, Michael. 2017. “A Conceptual Introduction to Hamiltonian Monte Carlo,” January. http://arxiv.org/abs/1701.02434.

———. 2018. “The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo.” Annalen Der Physik, March. https://doi.org/10.1002/andp.201700214.

Betancourt, Michael, Simon Byrne, Sam Livingstone, and Mark Girolami. 2017. “The Geometric Foundations of Hamiltonian Monte Carlo.” Bernoulli 23 (4A): 2257–98. https://doi.org/10.3150/16-BEJ810.

Calderhead, Ben. 2014. “A General Construction for Parallelizing Metropolis−Hastings Algorithms.” Proceedings of the National Academy of Sciences 111 (49): 17408–13. https://doi.org/10.1073/pnas.1408184111.

Carpenter, Bob, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael Betancourt. 2015. “The Stan Math Library: Reverse-Mode Automatic Differentiation in C++.” arXiv Preprint arXiv:1509.07164. http://arxiv.org/abs/1509.07164.

Diaconis, Persi, and David Freedman. 1999. “Iterated Random Functions.” SIAM Review 1 (1): 45–76. https://doi.org/10.1137/S0036144598338446.

Durmus, Alain, and Eric Moulines. 2016. “High-Dimensional Bayesian Inference via the Unadjusted Langevin Algorithm,” May. http://arxiv.org/abs/1605.01559.

Girolami, Mark, and Ben Calderhead. 2011. “Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73 (2): 123–214. https://doi.org/10.1111/j.1467-9868.2010.00765.x.

Goodrich, Ben, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Bob Carpenter, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. “Stan : A Probabilistic Programming Language.” Journal of Statistical Software 76 (1). https://doi.org/10.18637/jss.v076.i01.

Hodgkinson, Liam, Robert Salomone, and Fred Roosta. 2019. “Implicit Langevin Algorithms for Sampling from Log-Concave Densities,” March. http://arxiv.org/abs/1903.12322.

Jacob, Pierre E., John O’Leary, and Yves F. Atchadé. 2017. “Unbiased Markov Chain Monte Carlo with Couplings,” August. http://arxiv.org/abs/1708.03625.

Lele, S. R., B. Dennis, and F. Lutscher. 2007. “Data Cloning: Easy Maximum Likelihood Estimation for Complex Ecological Models Using Bayesian Markov Chain Monte Carlo Methods.” Ecology Letters 10 (7): 551. https://doi.org/10.1111/j.1461-0248.2007.01047.x.

Lele, Subhash R., Khurram Nadeem, and Byron Schmuland. 2010. “Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning.” Journal of the American Statistical Association 105 (492): 1617–25. https://doi.org/10.1198/jasa.2010.tm09757.

Liu, Jun S. 1996. “Metropolized Independent Sampling with Comparisons to Rejection Sampling and Importance Sampling.” Statistics and Computing 6 (2): 113–19. https://doi.org/10.1007/BF00162521.

Mangoubi, Oren, and Aaron Smith. 2017. “Rapid Mixing of Hamiltonian Monte Carlo on Strongly Log-Concave Distributions,” August. http://arxiv.org/abs/1708.07114.

Neal, Radford M. 1993. “Probabilistic Inference Using Markov Chain Monte Carlo Methods.” Technical Report CRGTR-93-1. Toronto Canada: Department of Computer Science, University of Toronto, https://www.cs.princeton.edu/courses/archive/fall07/cos597C/readings/Neal1993.pdf.

———. 2011. “MCMC Using Hamiltonian Dynamics.” In Handbook for Markov Chain Monte Carlo, edited by Steve Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng. Boca Raton: Taylor & Francis. http://arxiv.org/abs/1206.1901.

———. 2004. “Improving Asymptotic Variance of MCMC Estimators: Non-Reversible Chains Are Better,” July. http://arxiv.org/abs/math/0407281.

Norton, Richard A., and Colin Fox. 2016. “Tuning of MCMC with Langevin, Hamiltonian, and Other Stochastic Autoregressive Proposals,” October. http://arxiv.org/abs/1610.00781.

Propp, James Gary, and David Bruce Wilson. 1996. “Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics.” In Random Structures & Algorithms, 9:223–52. New York, NY, USA: John Wiley & Sons, Inc. https://doi.org/10.1002/(SICI)1098-2418(199608/09)9:1/2<223::AID-RSA14>3.0.CO;2-O.

———. 1998. “Coupling from the Past: A User’s Guide.” In Microsurveys in Discrete Probability, edited by David Aldous and James Gary Propp, 41:181–92. DIMACS Series in Discrete Mathematics and Theoretical Computer Science. Providence, Rhode Island: American Mathematical Society. https://doi.org/10.1090/dimacs/041.

Rubinstein, Reuven Y, and Dirk P Kroese. 2004. The Cross-Entropy Method a Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. New York, NY: Springer New York. http://dx.doi.org/10.1007/978-1-4757-4321-0.

Rubinstein, Reuven Y., and Dirk P. Kroese. 2016. Simulation and the Monte Carlo Method. 3 edition. Wiley Series in Probability and Statistics. Hoboken, New Jersey: Wiley.

Rubinstein, Reuven Y., Ad Ridder, and Radislav Vaisman. 2014. Fast Sequential Monte Carlo Methods for Counting and Optimization. Wiley Series in Probability and Statistics. Hoboken, New Jersey: Wiley.

Xifara, T., C. Sherlock, S. Livingstone, S. Byrne, and M. Girolami. 2014. “Langevin Diffusions and the Metropolis-Adjusted Langevin Algorithm.” Statistics & Probability Letters 91 (Supplement C): 14–19. https://doi.org/10.1016/j.spl.2014.04.002.

Yoshida, Ryo, and Mike West. 2010. “Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.” Journal of Machine Learning Research 11 (May): 1771–98. http://www.jmlr.org/papers/v11/yoshida10a.html.