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 *

164 related articles for article (PubMed ID: 38215839)

  • 1. Source term estimation for continuous plume dispersion in Fusion Field Trial-07: Bayesian inference probability adjoint inverse method.
    Zhang HL; Li B; Shang J; Wang WW; Zhao FY
    Sci Total Environ; 2024 Mar; 915():169802. PubMed ID: 38215839
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Bayesian inference and wind field statistical modeling applied to multiple source estimation.
    Albani RAS; Albani VVL; Gomes LES; Migon HS; Silva Neto AJ
    Environ Pollut; 2023 Mar; 321():121061. PubMed ID: 36702429
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Identifying spatiotemporal information of the point pollutant source indoors based on the adjoint-regularization method.
    Jing Y; Li F; Gu Z; Tang S
    Build Simul; 2023; 16(4):589-602. PubMed ID: 36789406
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Bayesian source term estimation of atmospheric releases in urban areas using LES approach.
    Xue F; Kikumoto H; Li X; Ooka R
    J Hazard Mater; 2018 May; 349():68-78. PubMed ID: 29414754
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method.
    Wang W; Ji C; Li C; Wu W; Gisen JIA
    Environ Sci Pollut Res Int; 2023 Jun; ():. PubMed ID: 37269522
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis.
    Zhu Y; Cao H; Gao Z; Chen Z
    Environ Pollut; 2024 Apr; 347():123448. PubMed ID: 38309421
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals.
    Liu X; Zhai Z
    Indoor Air; 2007 Dec; 17(6):419-38. PubMed ID: 18045267
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A general construction for parallelizing Metropolis-Hastings algorithms.
    Calderhead B
    Proc Natl Acad Sci U S A; 2014 Dec; 111(49):17408-13. PubMed ID: 25422442
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identification of illicit discharges in sewer networks by an SWMM-Bayesian coupled approach.
    Yang L; Huang B; Liu J
    Water Sci Technol; 2024 Aug; 90(3):951-967. PubMed ID: 39141044
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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; 10(6):515-542. PubMed ID: 34007522
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Estimating the granularity coefficient of a Potts-Markov random field within a Markov chain Monte Carlo algorithm.
    Pereyra M; Dobigeon N; Batatia H; Tourneret JY
    IEEE Trans Image Process; 2013 Jun; 22(6):2385-97. PubMed ID: 23475357
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Research on an Optimized Evaluation Method of the Bearing Capacity of Reinforced Concrete Beam Based on the Bayesian Theory.
    Wang L; Xiao Z; Yu F; Li W; Fu N
    Materials (Basel); 2023 Mar; 16(6):. PubMed ID: 36984368
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Practical guidelines for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC).
    Barido-Sottani J; Schwery O; Warnock RCM; Zhang C; Wright AM
    Open Res Eur; 2023; 3():204. PubMed ID: 38481771
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Novel and Highly Effective Bayesian Sampling Algorithm Based on the Auxiliary Variables to Estimate the Testlet Effect Models.
    Lu J; Zhang J; Zhang Z; Xu B; Tao J
    Front Psychol; 2021; 12():509575. PubMed ID: 34456774
    [TBL] [Abstract][Full Text] [Related]  

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

  • 18. Inverse estimation of finite-duration source release mass in river pollution accidents based on adjoint equation method.
    Jing P; Yang Z; Zhou W; Huai W; Lu X
    Environ Sci Pollut Res Int; 2020 May; 27(13):14679-14689. PubMed ID: 32052326
    [TBL] [Abstract][Full Text] [Related]  

  • 19. MCMC Methods for Parameter Estimation in ODE Systems for CAR-T Cell Cancer Therapy.
    Antonini E; Mu G; Sansaloni-Pastor S; Varma V; Kabak R
    Cancers (Basel); 2024 Sep; 16(18):. PubMed ID: 39335104
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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; 20(9):. PubMed ID: 33265731
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.