What we usually desire ergodicity results for.

Pierre E. Jacob, John O’Leary, Yves F. Atchadé made MCMC estimators without finite-time-bias, which is nice for parallelisation. (JaOA17)

## Refs

- Cald14
- Calderhead, B. (2014) A general construction for parallelizing Metropolis−Hastings algorithms.
*Proceedings of the National Academy of Sciences*, 111(49), 17408–17413. DOI. - JaOA17
- Jacob, P. E., O’Leary, J., & Atchadé, Y. F.(2017) Unbiased Markov chain Monte Carlo with couplings.
*ArXiv:1708.03625 [Stat]*. - Liu96
- Liu, J. S.(1996) Metropolized independent sampling with comparisons to rejection sampling and importance sampling.
*Statistics and Computing*, 6(2), 113–119. DOI. - Neal93
- Neal, R. M.(1993) Probabilistic inference using Markov chain Monte Carlo methods (No. Technical Report CRGTR-93-1). . Toronto Canada: Department of Computer Science, University of Toronto,
- NoFo16
- Norton, R. A., & Fox, C. (2016) Tuning of MCMC with Langevin, Hamiltonian, and other stochastic autoregressive proposals.
*ArXiv:1610.00781 [Math, Stat]*. - RuKr04
- Rubinstein, R. Y., & Kroese, D. P.(2004) The Cross-Entropy Method a Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. . New York, NY: Springer New York
- RuKr08
- Rubinstein, R. Y., & Kroese, D. P.(2008) Simulation and the monte carlo method. (2nd ed.). Hoboken, N.J: John Wiley & Sons
- RuRV14
- Rubinstein, R. Y., Ridder, A., & Vaisman, R. (2014) Fast sequential Monte Carlo methods for counting and optimization. . Hoboken, New Jersey: Wiley