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Monte Carlo methods / Markov chain Monte Carlo / Stochastic processes / Markov processes / Markov chain / Ergodicity / Metropolis–Hastings algorithm / Mixing / Gibbs sampling / Statistics / Probability and statistics / Markov models


Markov chain Monte Carlo: Some practical implications of theoretical results by Gareth O. Roberts* and
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Document Date: 2006-10-04 20:52:08


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File Size: 181,84 KB

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Company

Monte Carlo / Baxter / Statistical Laboratory / /

Country

Canada / United Kingdom / /

Currency

pence / /

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Facility

University of Toronto / University of Cambridge / /

IndustryTerm

straightforward algorithms / Basic algorithms / practice hybrid algorithms / reducible component algorithms / complicated algorithms / hybrid algorithm / guideline in determining which algorithms / constituent algorithms / hybrid algorithms / φ-irreducible algorithms / /

Organization

University of Toronto / Toronto / University of Cambridge / Department of Statistics / /

Person

Gareth O. Roberts / Jeffrey S. Rosenthal / /

ProvinceOrState

Ontario / /

Technology

Metropolis-Hastings algorithm / hybrid algorithm / φ-irreducible algorithms / reducible component algorithms / component algorithms / random-walk Metropolis algorithm / φ-irreducible MetropolisHastings algorithms / constituent algorithms / practice hybrid algorithms / The Langevin algorithm / simulation / quite straightforward algorithms / two algorithms / /

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