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 *

116 related articles for article (PubMed ID: 35910401)

  • 1. 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; 84(2):496-523. PubMed ID: 35910401
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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; 11(1):63. PubMed ID: 28646868
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method.
    Pan XP; Zhang GZ; Zhang JJ; Yin XY
    Pet Sci; 2017; 14(1):75-83. PubMed ID: 28239392
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Robust and Fast Markov Chain Monte Carlo Sampling of Diffusion MRI Microstructure Models.
    Harms RL; Roebroeck A
    Front Neuroinform; 2018; 12():97. PubMed ID: 30618702
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Searching for efficient Markov chain Monte Carlo proposal kernels.
    Yang Z; Rodríguez CE
    Proc Natl Acad Sci U S A; 2013 Nov; 110(48):19307-12. PubMed ID: 24218600
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals.
    Brofos JA; Gabrié M; Brubaker MA; Lederman RR
    Proc Mach Learn Res; 2022 Mar; 151():5949-5986. PubMed ID: 36789101
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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; 78(4 Pt 2):046704. PubMed ID: 18999558
    [TBL] [Abstract][Full Text] [Related]  

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

  • 10. On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions.
    Bandeira AS; Maillard A; Nickl R; Wang S
    Philos Trans A Math Phys Eng Sci; 2023 May; 381(2247):20220150. PubMed ID: 36970818
    [TBL] [Abstract][Full Text] [Related]  

  • 11. APT-MCMC, a C++/Python implementation of Markov Chain Monte Carlo for parameter identification.
    Zhang LA; Urbano A; Clermont G; Swigon D; Banerjee I; Parker RS
    Comput Chem Eng; 2018 Feb; 110():1-12. PubMed ID: 31427833
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Using model-based proposals for fast parameter inference on discrete state space, continuous-time Markov processes.
    Pooley CM; Bishop SC; Marion G
    J R Soc Interface; 2015 Jun; 12(107):. PubMed ID: 25994297
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. A Monte Carlo Metropolis-Hastings algorithm for sampling from distributions with intractable normalizing constants.
    Liang F; Jin IH
    Neural Comput; 2013 Aug; 25(8):2199-234. PubMed ID: 23607562
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.
    Liang F; Kim J; Song Q
    Technometrics; 2016; 58(3):604-318. PubMed ID: 29033469
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Learning Deep Generative Models With Doubly Stochastic Gradient MCMC.
    Du C; Zhu J; Zhang B
    IEEE Trans Neural Netw Learn Syst; 2018 Jul; 29(7):3084-3096. PubMed ID: 28678716
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Stability of noisy Metropolis-Hastings.
    Medina-Aguayo FJ; Lee A; Roberts GO
    Stat Comput; 2016; 26(6):1187-1211. PubMed ID: 32055107
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Automated Factor Slice Sampling.
    Tibbits MM; Groendyke C; Haran M; Liechty JC
    J Comput Graph Stat; 2014; 23(2):543-563. PubMed ID: 24955002
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.