scholarly journals Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition

2013 ◽  
Vol 28 (3) ◽  
pp. 360-375 ◽  
Author(s):  
Alicia A. Johnson ◽  
Galin L. Jones ◽  
Ronald C. Neath
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Geometric Ergodicity

scholarly journals Ergodicity of Markov chain Monte Carlo with reversible proposal

2017 ◽  
Vol 54 (2) ◽  
pp. 638-654 ◽  
Author(s):  
K. Kamatani
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Random Walk ◽  
Markov Chain Monte Carlo ◽  
Geometric Ergodicity ◽  
Suitable Transformation ◽  
Ergodic Properties ◽  
Random Walk Metropolis ◽  
Heavy Tailed ◽  
Random Walk Metropolis Algorithm

Abstract We describe the ergodic properties of some Metropolis–Hastings algorithms for heavy-tailed target distributions. The results of these algorithms are usually analyzed under a subgeometric ergodic framework, but we prove that the mixed preconditioned Crank–Nicolson (MpCN) algorithm has geometric ergodicity even for heavy-tailed target distributions. This useful property comes from the fact that, under a suitable transformation, the MpCN algorithm becomes a random-walk Metropolis algorithm.

On Markov Chain Monte Carlo Acceleration

1994 ◽  
Author(s):  
Alan E. Gelfand ◽  
Sujit K. Sahu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo
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