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Journal Abstract Search

298 related items for PubMed ID: 33826194

  • 1. Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models.
    Yao Y, Stephan KE.
    Hum Brain Mapp; 2021 Jul; 42(10):2973-2989. PubMed ID: 33826194
    [Abstract] [Full Text] [Related]

  • 2. A hierarchical model for integrating unsupervised generative embedding and empirical Bayes.
    Raman S, Deserno L, Schlagenhauf F, Stephan KE.
    J Neurosci Methods; 2016 Aug 30; 269():6-20. PubMed ID: 27141854
    [Abstract] [Full Text] [Related]

  • 3. Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE).
    Yao Y, Raman SS, Schiek M, Leff A, Frässle S, Stephan KE.
    Neuroimage; 2018 Oct 01; 179():604-619. PubMed ID: 29964187
    [Abstract] [Full Text] [Related]

  • 4. Performance of Hamiltonian Monte Carlo and No-U-Turn Sampler for estimating genetic parameters and breeding values.
    Nishio M, Arakawa A.
    Genet Sel Evol; 2019 Dec 10; 51(1):73. PubMed ID: 31823719
    [Abstract] [Full Text] [Related]

  • 5. Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems.
    Ballnus B, Hug S, Hatz K, Görlitz L, Hasenauer J, Theis FJ.
    BMC Syst Biol; 2017 Jun 24; 11(1):63. PubMed ID: 28646868
    [Abstract] [Full Text] [Related]

  • 6. Fast Bayesian whole-brain fMRI analysis with spatial 3D priors.
    Sidén P, Eklund A, Bolin D, Villani M.
    Neuroimage; 2017 Feb 01; 146():211-225. PubMed ID: 27876654
    [Abstract] [Full Text] [Related]

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  • 8. Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems.
    Kramer A, Calderhead B, Radde N.
    BMC Bioinformatics; 2014 Jul 28; 15(1):253. PubMed ID: 25066046
    [Abstract] [Full Text] [Related]

  • 9. Assessing convergence of Markov chain Monte Carlo simulations in hierarchical Bayesian models for population pharmacokinetics.
    Dodds MG, Vicini P.
    Ann Biomed Eng; 2004 Sep 28; 32(9):1300-13. PubMed ID: 15493516
    [Abstract] [Full Text] [Related]

  • 10. Bayesian hierarchical models for multi-level repeated ordinal data using WinBUGS.
    Qiu Z, Song PX, Tan M.
    J Biopharm Stat; 2002 May 28; 12(2):121-35. PubMed ID: 12413235
    [Abstract] [Full Text] [Related]

  • 11. Inversion of hierarchical Bayesian models using Gaussian processes.
    Lomakina EI, Paliwal S, Diaconescu AO, Brodersen KH, Aponte EA, Buhmann JM, Stephan KE.
    Neuroimage; 2015 Sep 28; 118():133-45. PubMed ID: 26048619
    [Abstract] [Full Text] [Related]

  • 12. Gradient-based MCMC samplers for dynamic causal modelling.
    Sengupta B, Friston KJ, Penny WD.
    Neuroimage; 2016 Jan 15; 125():1107-1118. PubMed ID: 26213349
    [Abstract] [Full Text] [Related]

  • 13. Inference of regulatory networks with a convergence improved MCMC sampler.
    Agostinho NB, Machado KS, Werhli AV.
    BMC Bioinformatics; 2015 Sep 24; 16():306. PubMed ID: 26399857
    [Abstract] [Full Text] [Related]

  • 14. Kullback-Leibler Markov chain Monte Carlo--a new algorithm for finite mixture analysis and its application to gene expression data.
    Tatarinova T, Bouck J, Schumitzky A.
    J Bioinform Comput Biol; 2008 Aug 24; 6(4):727-46. PubMed ID: 18763739
    [Abstract] [Full Text] [Related]

  • 15. Comparing variational Bayes with Markov chain Monte Carlo for Bayesian computation in neuroimaging.
    Nathoo FS, Lesperance ML, Lawson AB, Dean CB.
    Stat Methods Med Res; 2013 Aug 24; 22(4):398-423. PubMed ID: 22642986
    [Abstract] [Full Text] [Related]

  • 16. Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis.
    Guo P, Zhu B, Niu H, Wang Z, Liang Y, Chen Y, Zhang L, Ni H, Guo Y, Hay EHA, Gao X, Gao H, Wu X, Xu L, Li J.
    BMC Bioinformatics; 2018 Jan 03; 19(1):3. PubMed ID: 29298666
    [Abstract] [Full Text] [Related]

  • 17. Searching for convergence in phylogenetic Markov chain Monte Carlo.
    Beiko RG, Keith JM, Harlow TJ, Ragan MA.
    Syst Biol; 2006 Aug 03; 55(4):553-65. PubMed ID: 16857650
    [Abstract] [Full Text] [Related]

  • 18. Assessing the convergence of Markov Chain Monte Carlo methods: an example from evaluation of diagnostic tests in absence of a gold standard.
    Toft N, Innocent GT, Gettinby G, Reid SW.
    Prev Vet Med; 2007 May 16; 79(2-4):244-56. PubMed ID: 17292499
    [Abstract] [Full Text] [Related]

  • 19. Generative embedding for model-based classification of fMRI data.
    Brodersen KH, Schofield TM, Leff AP, Ong CS, Lomakina EI, Buhmann JM, Stephan KE.
    PLoS Comput Biol; 2011 Jun 16; 7(6):e1002079. PubMed ID: 21731479
    [Abstract] [Full Text] [Related]

  • 20. Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.
    Dhamala J, Arevalo HJ, Sapp J, Horácek BM, Wu KC, Trayanova NA, Wang L.
    Med Image Anal; 2018 Aug 16; 48():43-57. PubMed ID: 29843078
    [Abstract] [Full Text] [Related]

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