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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
3
Scopus citations
Overview
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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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Mathematics
Geometric Convergence
100%
Wasserstein Distance
90%
Contraction
59%
Markov chain
57%
Nonlinear Process
44%
Polish Space
43%
Random Effects Model
40%
Autoregressive Process
38%
Stationarity
37%
Stationary Distribution
35%
Vary
30%
State Space
29%
Upper bound
22%
Context
21%
Denote
21%
Standards
19%
Business & Economics
Markov Chain
76%
Contraction
73%
Autoregressive Process
45%
Stationary Distribution
45%
Random Effects Model
41%
Stationarity
38%
State Space
38%
Upper Bound
35%