BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 16599115)

  • 1. [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]  

  • 2. Water quality model parameter identification of an open channel in a long distance water transfer project based on finite difference, difference evolution and Monte Carlo.
    Shao D; Yang H; Xiao Y; Liu B
    Water Sci Technol; 2014; 69(3):587-94. PubMed ID: 24552732
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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; 48():43-57. PubMed ID: 29843078
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Stormwater quality models: performance and sensitivity analysis.
    Dotto CB; Kleidorfer M; Deletic A; Fletcher TD; McCarthy DT; Rauch W
    Water Sci Technol; 2010; 62(4):837-43. PubMed ID: 20729586
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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; 79(2-4):244-56. PubMed ID: 17292499
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Water quality modelling of the river Yamuna (India) using QUAL2E-UNCAS.
    Paliwal R; Sharma P; Kansal A
    J Environ Manage; 2007 Apr; 83(2):131-44. PubMed ID: 16697517
    [TBL] [Abstract][Full Text] [Related]  

  • 9. [Integrated model of nutrients for the Miyun Reservoir and its watershed].
    Wang JP; Su BL; Jia HF; Cheng ST; Yang ZS; Wu DW; Sun F
    Huan Jing Ke Xue; 2006 Jul; 27(7):1286-91. PubMed ID: 16881296
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Effect of uncertainties in agricultural working schedules and Monte-Carlo evaluation of the model input in basin-scale runoff model analysis of herbicides.
    Matsui Y; Inoue T; Matsushita T; Yamada T; Yamamoto M; Sumigama Y
    Water Sci Technol; 2005; 51(3-4):329-37. PubMed ID: 15850206
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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; Loáiciga HA
    Environ Monit Assess; 2020 Jan; 192(2):100. PubMed ID: 31912242
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Abrupt motion tracking via intensively adaptive Markov-chain Monte Carlo sampling.
    Zhou X; Lu Y; Lu J; Zhou J
    IEEE Trans Image Process; 2012 Feb; 21(2):789-801. PubMed ID: 21937350
    [TBL] [Abstract][Full Text] [Related]  

  • 15. AWTY (are we there yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics.
    Nylander JA; Wilgenbusch JC; Warren DL; Swofford DL
    Bioinformatics; 2008 Feb; 24(4):581-3. PubMed ID: 17766271
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Monte Carlo method for calculating Bayesian uncertainties in internal dosimetry.
    Puncher M; Birchall A
    Radiat Prot Dosimetry; 2008; 132(1):1-12. PubMed ID: 18806256
    [TBL] [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; 55(4):553-65. PubMed ID: 16857650
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [Parameter optimization of water quality model: implementation of genetic algorithm and its control parameters analysis].
    Wang JP; Cheng ST; Jia HF
    Huan Jing Ke Xue; 2005 May; 26(3):61-5. PubMed ID: 16124471
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Incorporating parameter uncertainty into Quantitative Microbial Risk Assessment (QMRA).
    Donald M; Mengersen K; Toze S; Sidhu JP; Cook A
    J Water Health; 2011 Mar; 9(1):10-26. PubMed ID: 21301111
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.