Autori
Mira, AntoniettaRigat, FabioTitolo
Parallel hierarchical sampling: a general-purpose class of multiple-chains MCMC algorithmsPeriodico
Università degli Studi dell'Insubria. Dipartimento di Economia. Quaderni di ricercaAnno:
2009 - Fascicolo:
5 - Pagina iniziale:
1 - Pagina finale:
55This paper introduces the Parallel Hierarchical Sampler (PHS), a
class of Markov chain Monte Carlo algorithms using several interacting
chains having the same target distribution but different mixing
properties. Unlike any single-chain MCMC algorithm, upon reaching
stationarity one of the PHS chains, which we call the “mother” chain,
attains exact Monte Carlo sampling of the target distribution of interest.
We empirically show that this translates in a dramatic improvement
in the sampler’s performance with respect to single-chain MCMC
algorithms. Convergence of the PHS joint transition kernel is proved
and its relationships with single-chain samplers, Parallel Tempering
(PT) and variable augmentation algorithms are discussed. We then
provide two illustrative examples comparing the accuracy of PHS with that of various Metropolis-Hastings and PT for sampling multimodal
mixtures of multivariate Gaussian densities and for ’banana-shaped’
multivariate distributions with heavy tails. Finally, PHS is applied
to approximate inferences for two Bayesian model uncertainty problems,
namely selection of main effects for a linear Gaussian multiple
regression model and inference for the structure of an exponential treed
survival model.
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