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Journal Abstract Search

419 related items for PubMed ID: 25422442

  • 1.
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  • 3. Convergence Rates for the Constrained Sampling via Langevin Monte Carlo.
    Zhu Y.
    Entropy (Basel); 2023 Aug 18; 25(8):. PubMed ID: 37628264
    [Abstract] [Full Text] [Related]

  • 4. Variational method for estimating the rate of convergence of Markov-chain Monte Carlo algorithms.
    Casey FP, Waterfall JJ, Gutenkunst RN, Myers CR, Sethna JP.
    Phys Rev E Stat Nonlin Soft Matter Phys; 2008 Oct 18; 78(4 Pt 2):046704. PubMed ID: 18999558
    [Abstract] [Full Text] [Related]

  • 5. A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.
    Liang F, Kim J, Song Q.
    Technometrics; 2016 Oct 18; 58(3):604-318. PubMed ID: 29033469
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  • 6. Two-Stage Metropolis-Hastings for Tall Data.
    Payne RD, Mallick BK.
    J Classif; 2018 Apr 18; 35(1):29-51. PubMed ID: 30287977
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  • 7. Searching for efficient Markov chain Monte Carlo proposal kernels.
    Yang Z, Rodríguez CE.
    Proc Natl Acad Sci U S A; 2013 Nov 26; 110(48):19307-12. PubMed ID: 24218600
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  • 8. BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO.
    Catanach TA, Vo HD, Munsky B.
    Int J Uncertain Quantif; 2020 Nov 26; 10(6):515-542. PubMed ID: 34007522
    [Abstract] [Full Text] [Related]

  • 9. Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference.
    Altekar G, Dwarkadas S, Huelsenbeck JP, Ronquist F.
    Bioinformatics; 2004 Feb 12; 20(3):407-15. PubMed ID: 14960467
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  • 10. Fitting complex population models by combining particle filters with Markov chain Monte Carlo.
    Knape J, de Valpine P.
    Ecology; 2012 Feb 12; 93(2):256-63. PubMed ID: 22624307
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  • 11. The Barker proposal: Combining robustness and efficiency in gradient-based MCMC.
    Livingstone S, Zanella G.
    J R Stat Soc Series B Stat Methodol; 2022 Apr 12; 84(2):496-523. PubMed ID: 35910401
    [Abstract] [Full Text] [Related]

  • 12. Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data.
    Saraiva EF, Suzuki AK, Milan LA.
    Entropy (Basel); 2018 Aug 27; 20(9):. PubMed ID: 33265731
    [Abstract] [Full Text] [Related]

  • 13. 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
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  • 14. Applying diffusion-based Markov chain Monte Carlo.
    Herbei R, Paul R, Berliner LM.
    PLoS One; 2017 Jun 24; 12(3):e0173453. PubMed ID: 28301529
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  • 15. de Finetti Priors using Markov chain Monte Carlo computations.
    Bacallado S, Diaconis P, Holmes S.
    Stat Comput; 2015 Jul 01; 25(4):797-808. PubMed ID: 26412947
    [Abstract] [Full Text] [Related]

  • 16. A quasi-Monte Carlo Metropolis algorithm.
    Owen AB, Tribble SD.
    Proc Natl Acad Sci U S A; 2005 Jun 21; 102(25):8844-9. PubMed ID: 15956207
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  • 17. Adaptive Metropolis-coupled MCMC for BEAST 2.
    Müller NF, Bouckaert RR.
    PeerJ; 2020 Jun 21; 8():e9473. PubMed ID: 32995072
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  • 18. Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks.
    McNaughton B, Milošević MV, Perali A, Pilati S.
    Phys Rev E; 2020 May 21; 101(5-1):053312. PubMed ID: 32575304
    [Abstract] [Full Text] [Related]

  • 19. An algorithm for Monte Carlo estimation of genotype probabilities on complex pedigrees.
    Lin S, Thompson E, Wijsman E.
    Ann Hum Genet; 1994 Oct 21; 58(4):343-57. PubMed ID: 7864590
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