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10.1016/j.neuroimage.2015.11.015 http://scihub22266oqcxt.onion/10.1016/j.neuroimage.2015.11.015 C4767224!4767224!26569570     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Neuroimage 2016 ; 128 (ä): 413-31 Nephropedia Template TP {{tp|p=26569570|t=2016. Bayesian model reduction and empirical Bayes for group (DCM) studies |pdf=|usr=}}{{26569570}} gab.com Text Bayesian model reduction and empirical Bayes for group (DCM) studies JO: Neuroimage http://www.kidney.de/pget.php?&tw=1&pmid=26569570 #FOAvip This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level ? e.g., dynamic causal models ? and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. Twit Text FOAVip Bayesian model reduction and empirical Bayes for group (DCM) studies ????Neuroimage http://www.kidney.de/pget.php?&tw=1&pmid=26569570 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26569570 #FOAvip English Wikipedia <ref>{{Cite pmid|26569570}}</ref> Bayesian model reduction and empirical Bayes for group (DCM) studies #MMPMID26569570 Friston KJ; Litvak V; Oswal A; Razi A; Stephan KE; van Wijk BC; Ziegler G; Zeidman P Neuroimage 2016[Mar]; 128 (ä): 413-31 PMID26569570show ga This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level ? e.g., dynamic causal models ? and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. äBayesian model reduction and empirical Bayes for group (DCM) studies (epmc).pdf DeepDyve Pubget Overpricing