On the applicability of regenerative simulation in Markov chain Monte Carlo
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Cited by:
- Bhattacharya, Sourabh, 2008. "Consistent estimation of the accuracy of importance sampling using regenerative simulation," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2522-2527, October.
- Johnson, Alicia A. & Jones, Galin L., 2015. "Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 325-342.
- Emilio De Santis & Mauro Piccioni, 2008. "Exact Simulation for Discrete Time Spin Systems and Unilateral Fields," Methodology and Computing in Applied Probability, Springer, vol. 10(1), pages 105-120, March.
- Pierre E. Jacob & John O’Leary & Yves F. Atchadé, 2020. "Unbiased Markov chain Monte Carlo methods with couplings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 543-600, July.
- M. A. Fleischer & S. H. Jacobson, 2002. "Scale Invariance Properties in the Simulated Annealing Algorithm," Methodology and Computing in Applied Probability, Springer, vol. 4(3), pages 219-241, September.
- Pierre Jacob & Christian P. Robert & Murray H. Smith, 2010. "Using Parallel Computation to Improve Independent Metropolis-Hastings Based Estimation," Working Papers 2010-44, Center for Research in Economics and Statistics.
- Bertail, Patrice & Clemencon, Stephan, 2008. "Approximate regenerative-block bootstrap for Markov chains," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2739-2756, January.
- Yu Hang Jiang & Tong Liu & Zhiya Lou & Jeffrey S. Rosenthal & Shanshan Shangguan & Fei Wang & Zixuan Wu, 2022. "Markov Chain Confidence Intervals and Biases," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(1), pages 1-29, March.
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