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- W2098481489 abstract "MCMC methods are used in Bayesian statistics not only to sample from posterior distributions but also to estimate expectations. Un- derlying functions are most often dened on a continuous state space and can be unbounded. We consider a regenerative setting and Monte Carlo estimators based on i.i.d. blocks of a Markov chain trajectory. The main result is an inequality for the mean square error. We also consider con- dence bounds. We rst derive the results in terms of the asymptotic vari- ance and then bound the asymptotic variance for both uniformly ergodic and geometrically ergodic Markov chains. AMS 2000 subject classications: Primary 60J10, 65C05; secondary 62L12." @default.
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- W2098481489 date "2009-07-28" @default.
- W2098481489 modified "2023-09-27" @default.
- W2098481489 title "Nonasymptotic bounds on the estimation error for regenerative MCMC algorithms" @default.
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