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There are 26306 results for: content related to: On thinning of chains in MCMC


    Ecological Applications

    Volume 18, Issue 8, December 2008, Pages: 1901–1913, J. Wilson White, Julie D. Standish, Simon R. Thorrold and Robert R. Warner

    Version of Record online : 1 DEC 2008, DOI: 10.1890/07-1792.1

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    Hierarchical analysis of spatially autocorrelated ecological data using integrated nested Laplace approximation

    Methods in Ecology and Evolution

    Volume 3, Issue 5, October 2012, Pages: 921–929, Julien Beguin, Sara Martino, Håvard Rue and Steven G. Cumming

    Version of Record online : 10 MAY 2012, DOI: 10.1111/j.2041-210X.2012.00211.x

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    Particle Markov chain Monte Carlo methods

    Journal of the Royal Statistical Society: Series B (Statistical Methodology)

    Volume 72, Issue 3, June 2010, Pages: 269–342, Christophe Andrieu, Arnaud Doucet and Roman Holenstein

    Version of Record online : 20 MAY 2010, DOI: 10.1111/j.1467-9868.2009.00736.x

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    Comparing parent–offspring regression with frequentist and Bayesian animal models to estimate heritability in wild populations: a simulation study for Gaussian and binary traits

    Methods in Ecology and Evolution

    Volume 4, Issue 3, March 2013, Pages: 260–275, Pierre de Villemereuil, Olivier Gimenez and Blandine Doligez

    Version of Record online : 20 DEC 2012, DOI: 10.1111/2041-210X.12011

  5. MC(MC)MC: exploring Monte Carlo integration within MCMC for mark–recapture models with individual covariates

    Methods in Ecology and Evolution

    Volume 5, Issue 12, December 2014, Pages: 1305–1315, Simon Bonner and Matthew Schofield

    Version of Record online : 27 AUG 2013, DOI: 10.1111/2041-210X.12095

  6. Practical Use of MCMC Methods: Lessons from a Case Study

    Biometrical Journal

    Volume 44, Issue 4, June 2002, Pages: 433–455, Grietje Zuur, Paul H. Garthwaite and Rob J. Fryer

    Version of Record online : 4 JUN 2002, DOI: 10.1002/1521-4036(200206)44:4<433::AID-BIMJ433>3.0.CO;2-4

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    Rapid Bayesian inference of heritability in animal models without convergence problems

    Methods in Ecology and Evolution

    Volume 4, Issue 11, November 2013, Pages: 1037–1046, Jon Ahlinder and Mikko J. Sillanpää

    Version of Record online : 18 OCT 2013, DOI: 10.1111/2041-210X.12113

  8. Monte Carlo Inference for State–Space Models of Wild Animal Populations


    Volume 65, Issue 2, June 2009, Pages: 572–583, Ken B. Newman, Carmen Fernández, Len Thomas and Stephen T. Buckland

    Version of Record online : 28 JUN 2008, DOI: 10.1111/j.1541-0420.2008.01073.x

  9. Data-driven model reduction for the Bayesian solution of inverse problems

    International Journal for Numerical Methods in Engineering

    Volume 102, Issue 5, 4 May 2015, Pages: 966–990, Tiangang Cui, Youssef M. Marzouk and Karen E. Willcox

    Version of Record online : 15 AUG 2014, DOI: 10.1002/nme.4748


    Ecological Applications

    Volume 13, Issue 3, June 2003, Pages: 763–775, Benjamin Bolker, Toshinori Okuyama, Karen Bjorndal and Alan Bolten

    Version of Record online : 1 JUN 2003, DOI: 10.1890/1051-0761(2003)013[0763:STSEUG]2.0.CO;2

  11. Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods

    Educational Measurement: Issues and Practice

    Volume 26, Issue 4, Winter 2007, Pages: 38–51, Jee-Seon Kim and Daniel M. Bolt

    Version of Record online : 14 NOV 2007, DOI: 10.1111/j.1745-3992.2007.00107.x

  12. A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

    JAWRA Journal of the American Water Resources Association

    Volume 51, Issue 5, October 2015, Pages: 1372–1393, René A. Camacho, James L. Martin, William McAnally, Jairo Díaz-Ramirez, Hugo Rodriguez, Peter Sucsy and Song Zhang

    Version of Record online : 15 JUN 2015, DOI: 10.1111/1752-1688.12319

  13. Implementing Markov Chain Monte Carlo

    Bayesian Analysis for the Social Sciences

    Simon Jackman, Pages: 251–297, 2009

    Published Online : 26 OCT 2009, DOI: 10.1002/9780470686621.ch6

  14. Identifiability of parameters and behaviour of MCMC chains: a case study using the reaction norm model

    Journal of Animal Breeding and Genetics

    Volume 126, Issue 2, April 2009, Pages: 92–102, M.M. Shariati, I.R. Korsgaard and D. Sorensen

    Version of Record online : 2 FEB 2009, DOI: 10.1111/j.1439-0388.2008.00773.x

  15. Markov Chain Monte Carlo, Recent Developments

    Standard Article

    Encyclopedia of Biostatistics

    K. Mengersen

    Published Online : 15 JUL 2005, DOI: 10.1002/0470011815.b2a14039

  16. Introduction: The Bayesian Method, its Benefits and Implementation

    Bayesian Statistical Modelling, Second Edition

    Peter Congdon, Pages: 1–23, 2007

    Published Online : 11 MAY 2007, DOI: 10.1002/9780470035948.ch1

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    Estimating patterns and drivers of infection prevalence and intensity when detection is imperfect and sampling error occurs

    Methods in Ecology and Evolution

    Volume 3, Issue 5, October 2012, Pages: 850–859, David A. W. Miller, Brooke L. Talley, Karen R. Lips and Evan H. Campbell Grant

    Version of Record online : 7 JUN 2012, DOI: 10.1111/j.2041-210X.2012.00216.x

  18. Exploring heterogeneity in tumour data using Markov chain Monte Carlo

    Statistics in Medicine

    Volume 22, Issue 10, 30 May 2003, Pages: 1691–1707, Mathisca C. M. de Gunst, Anup Dewanji and E. Georg Luebeck

    Version of Record online : 17 APR 2003, DOI: 10.1002/sim.1441

  19. Markov Chain Monte Carlo

    Computational Statistics, Second Edition

    Geof H. Givens, Jennifer A. Hoeting, Pages: 201–235, 2013

    Published Online : 12 MAR 2013, DOI: 10.1002/9781118555552.ch7

  20. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data

    Geophysical Journal International

    Volume 187, Issue 1, October 2011, Pages: 252–272, Burke J. Minsley

    Version of Record online : 29 AUG 2011, DOI: 10.1111/j.1365-246X.2011.05165.x