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Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions
Qian Qin
, James P. Hobert
Statistics (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
6
Scopus citations
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Dive into the research topics of 'Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions'. Together they form a unique fingerprint.
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Keyphrases
Distance-based
100%
Markov Chain
100%
Drift Condition
100%
Contraction Condition
100%
Wasserstein Distance
100%
Convergence Bounds
100%
Generalized Contraction
100%
Generalized Drift
100%
Geometric Convergence
100%
State Space
50%
Random Effects Model
50%
Stationary Distribution
50%
Stationarity
50%
Gibbs Sampling
50%
Polish Space
50%
Geometric Bounds
50%
Nonlinear Autoregressive Process
50%
Mathematics
Contraction Condition
100%
Wasserstein Distance
100%
Markov Chain
100%
Nonlinear
50%
Stationarity
50%
Upper Bound
50%
Autoregressive Model
50%
Random Effects Model
50%
Gibbs Algorithm
50%
Polish Space
50%