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- W58524104 abstract "In MCMC methods, such as the Metropolis-Hastings (MH) algorithm, the Gibbs sampler, or recent adaptive methods, many different strategies can be proposed, often asso- ciated in practice to unknown rates of convergence. In this paper we propose a simulation- based methodology to compare these rates of convergence, grounded on an entropy criterion computed from parallel (i.i.d.) simulated Markov chains coming from each candidate strat- egy. Our criterion determines the most efficient strategy among the candidates. Theoreti- cally, we give for the MH algorithm general conditions under which its successive densities satisfy adequate smoothness and tail properties, so that this entropy criterion can be esti- mated consistently using kernel density estimate and Monte Carlo integration. Simulated and actual examples in moderate dimensions are provided to illustrate this approach." @default.
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- W58524104 date "2007-04-01" @default.
- W58524104 modified "2023-09-27" @default.
- W58524104 title "How to compare MCMC simulation strategies?" @default.
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