Comparison of methodologies to assess the convergence of Markov chain Monte Carlo methods
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Publication:959355
DOI10.1016/j.csda.2005.04.018zbMath1445.62005OpenAlexW2079678892MaRDI QIDQ959355
Bernard Bobée, Anne-Catherine Favre, Salaheddine El Adlouni
Publication date: 11 December 2008
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2005.04.018
Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)
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Uses Software
Cites Work
- Unnamed Item
- Discretization and MCMC. Convergence assessment
- Inference from iterative simulation using multiple sequences
- Markov chains for exploring posterior distributions. (With discussion)
- Large sample confidence regions based on subsamples under minimal assumptions
- Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
- Simulation Run Length Control in the Presence of an Initial Transient
- Miscellanea. On quantile estimation and Markov chain Monte Carlo convergence
- Equation of State Calculations by Fast Computing Machines
- The Monte Carlo Method
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