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10.1080/10618600.2014.902764 http://scihub22266oqcxt.onion/10.1080/10618600.2014.902764 C4521456!4521456 !26240515     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26240515 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Comput+Graph+Stat 2015 ; 24 (2 ): 357-378 Nephropedia Template TP {{tp|p=26240515 |t=2015. Markov Chain Monte Carlo from Lagrangian Dynamics |pdf=|usr=}}{{26240515 }} gab.com Text Markov Chain Monte Carlo from Lagrangian Dynamics JO: J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=26240515 #FOAvip Hamiltonian Monte Carlo (HMC) improves the computational e ciency of the Metropolis-Hastings algorithm by reducing its random walk behavior. Riemannian HMC (RHMC) further improves the performance of HMC by exploiting the geometric properties of the parameter space. However, the geometric integrator used for RHMC involves implicit equations that require fixed-point iterations. In some cases, the computational overhead for solving implicit equations undermines RHMC's benefits. In an attempt to circumvent this problem, we propose an explicit integrator that replaces the momentum variable in RHMC by velocity. We show that the resulting transformation is equivalent to transforming Riemannian Hamiltonian dynamics to Lagrangian dynamics. Experimental results suggests that our method improves RHMC's overall computational e ciency in the cases considered. All computer programs and data sets are available online (http://www.ics.uci.edu/~babaks/Site/Codes.html) in order to allow replication of the results reported in this paper. Twit Text FOAVip Markov Chain Monte Carlo from Lagrangian Dynamics ????J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=26240515 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26240515 #FOAvip English Wikipedia <ref>{{Cite pmid|26240515 }}</ref> Markov Chain Monte Carlo from Lagrangian Dynamics #MMPMID26240515 Lan S ; Stathopoulos V ; Shahbaba B ; Girolami M J Comput Graph Stat 2015[Apr]; 24 (2 ): 357-378 PMID26240515 show ga Hamiltonian Monte Carlo (HMC) improves the computational e ciency of the Metropolis-Hastings algorithm by reducing its random walk behavior. Riemannian HMC (RHMC) further improves the performance of HMC by exploiting the geometric properties of the parameter space. However, the geometric integrator used for RHMC involves implicit equations that require fixed-point iterations. In some cases, the computational overhead for solving implicit equations undermines RHMC's benefits. In an attempt to circumvent this problem, we propose an explicit integrator that replaces the momentum variable in RHMC by velocity. We show that the resulting transformation is equivalent to transforming Riemannian Hamiltonian dynamics to Lagrangian dynamics. Experimental results suggests that our method improves RHMC's overall computational e ciency in the cases considered. All computer programs and data sets are available online (http://www.ics.uci.edu/~babaks/Site/Codes.html) in order to allow replication of the results reported in this paper. äMarkov Chain Monte Carlo from Lagrangian Dynamics (epmc).pdf DeepDyve Pubget Overpricing