These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

152 related articles for article (PubMed ID: 31912242)

  • 1. Analysis of the effect of inputs uncertainty on riverine water temperature predictions with a Markov chain Monte Carlo (MCMC) algorithm.
    Abdi B; Bozorg-Haddad O; LoƔiciga HA
    Environ Monit Assess; 2020 Jan; 192(2):100. PubMed ID: 31912242
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Uncertainty analysis of model inputs in riverine water temperature simulations.
    Abdi B; Bozorg-Haddad O; Chu X
    Sci Rep; 2021 Oct; 11(1):19908. PubMed ID: 34620930
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A Bayesian approach for evaluation of the effect of water quality model parameter uncertainty on TMDLs: A case study of Miyun Reservoir.
    Liang S; Jia H; Xu C; Xu T; Melching C
    Sci Total Environ; 2016 Aug; 560-561():44-54. PubMed ID: 27093122
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis.
    Zhu Y; Chen Z; Asif Z
    Environ Pollut; 2021 Sep; 285():117497. PubMed ID: 34380214
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A stochastic regression approach to analyzing thermodynamic uncertainty in chemical speciation modeling.
    Weber CL; Vanbriesen JM; Small MS
    Environ Sci Technol; 2006 Jun; 40(12):3872-8. PubMed ID: 16830555
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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; 19(1):3. PubMed ID: 29298666
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A gradient Markov chain Monte Carlo algorithm for computing multivariate maximum likelihood estimates and posterior distributions: mixture dose-response assessment.
    Li R; Englehardt JD; Li X
    Risk Anal; 2012 Feb; 32(2):345-59. PubMed ID: 21906114
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [Markov Chain Monte Carlo scheme for parameter uncertainty analysis in water quality model].
    Wang JP; Cheng ST; Jia HF
    Huan Jing Ke Xue; 2006 Jan; 27(1):24-30. PubMed ID: 16599115
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A simple introduction to Markov Chain Monte-Carlo sampling.
    van Ravenzwaaij D; Cassey P; Brown SD
    Psychon Bull Rev; 2018 Feb; 25(1):143-154. PubMed ID: 26968853
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Uncertainty quantification and atmospheric source estimation with a discrepancy-based and a state-dependent adaptative MCMC.
    Albani RAS; Albani VVL; Migon HS; Silva Neto AJ
    Environ Pollut; 2021 Dec; 290():118039. PubMed ID: 34467885
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Bayesian-based calibration for water quality model parameters.
    Bai B; Dong F; Peng W; Liu X
    Water Environ Res; 2023 Oct; 95(10):e10936. PubMed ID: 37807852
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.
    Fang W; Wang Z; Giles MB; Jackson CH; Welton NJ; Andrieu C; Thom H
    Med Decis Making; 2022 Feb; 42(2):168-181. PubMed ID: 34231446
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach.
    Awate SP; Garg S; Jena R
    Med Image Anal; 2019 Jul; 55():181-196. PubMed ID: 31085445
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Riverine nitrate source apportionment using dual stable isotopes in a drinking water source watershed of southeast China.
    Shang X; Huang H; Mei K; Xia F; Chen Z; Yang Y; Dahlgren RA; Zhang M; Ji X
    Sci Total Environ; 2020 Jul; 724():137975. PubMed ID: 32247143
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Applying diffusion-based Markov chain Monte Carlo.
    Herbei R; Paul R; Berliner LM
    PLoS One; 2017; 12(3):e0173453. PubMed ID: 28301529
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models.
    Johnson RT; Lakeland D; Finley JM
    J Neuroeng Rehabil; 2022 Mar; 19(1):34. PubMed ID: 35321736
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation.
    Bresson G; Chaturvedi A; Rahman MA; Shalabh
    Int J Biostat; 2020 Sep; 17(1):75-97. PubMed ID: 32949454
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid-dynamics model of the pulmonary circulation.
    Paun LM; Husmeier D
    Int J Numer Method Biomed Eng; 2021 Feb; 37(2):e3421. PubMed ID: 33249755
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis-Hastings algorithm.
    Liu X; Xia C; Tang Y; Tu J; Wang H
    Water Sci Technol; 2021 Mar; 83(5):1085-1102. PubMed ID: 33724938
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.
    Schmidt PJ; Pintar KD; Fazil AM; Topp E
    Risk Anal; 2013 Sep; 33(9):1677-93. PubMed ID: 23311599
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.