Perfect Slice Samplers
Copyright policy )Perfect Slice Samplers
Summary Perfect sampling allows the exact simulation of random variables from the stationary measure of a Markov chain. By exploiting monotonicity properties of the slice sampler we show that a perfect version of the algorithm can be easily implemented, at least when the target distribution is bounded. Various extensions, including perfect product slice samplers, and examples of applications are discussed.
- Università dell Insubria (Varese) Italy
- Aalborg University Denmark
- Lancaster University United Kingdom
- Aalborg University Library (AUB) Denmark
- University of Insubria Italy
auxiliary variables, algorithm, MCMC, Monte Carlo methods, coupling from the past, Dynamic lattice systems (kinetic Ising, etc.) and systems on graphs in time-dependent statistical mechanics, Swendsen-Wang algorithm, Markov chain Monte Carlo, Computational methods in Markov chains, Ising model, perfect simulation, Numerical methods of time-dependent statistical mechanics, random fields, Numerical analysis or methods applied to Markov chains, Random fields, slice sampler, automodels
auxiliary variables, algorithm, MCMC, Monte Carlo methods, coupling from the past, Dynamic lattice systems (kinetic Ising, etc.) and systems on graphs in time-dependent statistical mechanics, Swendsen-Wang algorithm, Markov chain Monte Carlo, Computational methods in Markov chains, Ising model, perfect simulation, Numerical methods of time-dependent statistical mechanics, random fields, Numerical analysis or methods applied to Markov chains, Random fields, slice sampler, automodels
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).36 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!