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10.1371/journal.pone.0173453 http://scihub22266oqcxt.onion/10.1371/journal.pone.0173453 C5354282!5354282!28301529     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28301529.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+One 2017 ; 12 (3): ä Nephropedia Template TP {{tp|p=28301529|t=2017. Applying diffusion-based Markov chain Monte Carlo |pdf=|usr=}}{{28301529}} gab.com Text Applying diffusion-based Markov chain Monte Carlo JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28301529 #FOAvip We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC. A variety of motivations for the approach are reviewed in the context of Bayesian analysis. In particular, implementation of diffusion MCMC is very simple to set-up, even in the presence of nonlinear models and non-conjugate priors. Also, it requires comparatively little problem-specific tuning. We implement the algorithm and assess its performance for both a test case and a glaciological application. Our results demonstrate that in some settings, diffusion MCMC is a faster alternative to a general Metropolis-Hastings algorithm. Twit Text FOAVip Applying diffusion-based Markov chain Monte Carlo ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28301529 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28301529 #FOAvip English Wikipedia <ref>{{Cite pmid|28301529}}</ref> Applying diffusion-based Markov chain Monte Carlo #MMPMID28301529 Herbei R; Paul R; Berliner LM PLoS One 2017[]; 12 (3): ä PMID28301529show ga We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC. A variety of motivations for the approach are reviewed in the context of Bayesian analysis. In particular, implementation of diffusion MCMC is very simple to set-up, even in the presence of nonlinear models and non-conjugate priors. Also, it requires comparatively little problem-specific tuning. We implement the algorithm and assess its performance for both a test case and a glaciological application. Our results demonstrate that in some settings, diffusion MCMC is a faster alternative to a general Metropolis-Hastings algorithm. äApplying diffusion-based Markov chain Monte Carlo (epmc).pdf DeepDyve Pubget Overpricing