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.
126 related articles for article (PubMed ID: 36280097)
1. Bayesian connective field modeling using a Markov Chain Monte Carlo approach. Invernizzi A; Haak KV; Carvalho JC; Renken RJ; Cornelissen FW Neuroimage; 2022 Dec; 264():119688. PubMed ID: 36280097 [TBL] [Abstract][Full Text] [Related]
2. Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T. Invernizzi A; Gravel N; Haak KV; Renken RJ; Cornelissen FW Front Neurosci; 2021; 15():625309. PubMed ID: 33692669 [TBL] [Abstract][Full Text] [Related]
3. Cortical connective field estimates from resting state fMRI activity. Gravel N; Harvey B; Nordhjem B; Haak KV; Dumoulin SO; Renken R; Curčić-Blake B; Cornelissen FW Front Neurosci; 2014; 8():339. PubMed ID: 25400541 [TBL] [Abstract][Full Text] [Related]
4. Fast Bayesian whole-brain fMRI analysis with spatial 3D priors. Sidén P; Eklund A; Bolin D; Villani M Neuroimage; 2017 Feb; 146():211-225. PubMed ID: 27876654 [TBL] [Abstract][Full Text] [Related]
5. A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data. Chen S; Bowman FD; Mayberg HS Biometrics; 2016 Jun; 72(2):596-605. PubMed ID: 26501687 [TBL] [Abstract][Full Text] [Related]
6. Efficient Markov chain Monte Carlo methods for decoding neural spike trains. Ahmadian Y; Pillow JW; Paninski L Neural Comput; 2011 Jan; 23(1):46-96. PubMed ID: 20964539 [TBL] [Abstract][Full Text] [Related]
7. 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]
8. Assessing convergence of Markov chain Monte Carlo simulations in hierarchical Bayesian models for population pharmacokinetics. Dodds MG; Vicini P Ann Biomed Eng; 2004 Sep; 32(9):1300-13. PubMed ID: 15493516 [TBL] [Abstract][Full Text] [Related]
9. Empirical Markov Chain Monte Carlo Bayesian analysis of fMRI data. de Pasquale F; Del Gratta C; Romani GL Neuroimage; 2008 Aug; 42(1):99-111. PubMed ID: 18538586 [TBL] [Abstract][Full Text] [Related]
10. A Bayesian non-parametric Potts model with application to pre-surgical FMRI data. Johnson TD; Liu Z; Bartsch AJ; Nichols TE Stat Methods Med Res; 2013 Aug; 22(4):364-81. PubMed ID: 22627277 [TBL] [Abstract][Full Text] [Related]
11. 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]
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. 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]
14. Hierarchical Bayesian modeling and Markov chain Monte Carlo sampling for tuning-curve analysis. Cronin B; Stevenson IH; Sur M; Körding KP J Neurophysiol; 2010 Jan; 103(1):591-602. PubMed ID: 19889855 [TBL] [Abstract][Full Text] [Related]
15. Simultaneous estimation of population receptive field and hemodynamic parameters from single point BOLD responses using Metropolis-Hastings sampling. Adaszewski S; Slater D; Melie-Garcia L; Draganski B; Bogorodzki P Neuroimage; 2018 May; 172():175-193. PubMed ID: 29414493 [TBL] [Abstract][Full Text] [Related]