![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 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](https://www.pdfsearch.io/img/5a19499fddb2d779552a66069c8a2ee2.jpg)
| Document Date: 2006-10-04 20:52:08 Open Document File Size: 181,84 KBShare Result on Facebook
Company Monte Carlo / Baxter / Statistical Laboratory / / Country Canada / United Kingdom / / Currency pence / / / 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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