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10.1038/s41467-018-04241-5 http://scihub22266oqcxt.onion/10.1038/s41467-018-04241-5 C5935699!5935699!29728622     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2018 ; 9 (ä): ä Nephropedia Template TP {{tp|p=29728622|t=2018. Bayesian model selection for complex dynamic systems |pdf=|usr=}}{{29728622}} gab.com Text Bayesian model selection for complex dynamic systems JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=29728622 #FOAvip Time series generated by complex systems like financial markets and the earth?s atmosphere often represent superstatistical random walks: on short time scales, the data follow a simple low-level model, but the model parameters are not constant and can fluctuate on longer time scales according to a high-level model. While the low-level model is often dictated by the type of the data, the high-level model, which describes how the parameters change, is unknown in most cases. Here we present a computationally efficient method to infer the time course of the parameter variations from time-series with short-range correlations. Importantly, this method evaluates the model evidence to objectively select between competing high-level models. We apply this method to detect anomalous price movements in financial markets, characterize cancer cell invasiveness, identify historical policies relevant for working safety in coal mines, and compare different climate change scenarios to forecast global warming. Twit Text FOAVip Bayesian model selection for complex dynamic systems ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=29728622 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29728622 #FOAvip English Wikipedia <ref>{{Cite pmid|29728622}}</ref> Bayesian model selection for complex dynamic systems #MMPMID29728622 Mark C; Metzner C; Lautscham L; Strissel PL; Strick R; Fabry B Nat Commun 2018[]; 9 (ä): ä PMID29728622show ga Time series generated by complex systems like financial markets and the earth?s atmosphere often represent superstatistical random walks: on short time scales, the data follow a simple low-level model, but the model parameters are not constant and can fluctuate on longer time scales according to a high-level model. While the low-level model is often dictated by the type of the data, the high-level model, which describes how the parameters change, is unknown in most cases. Here we present a computationally efficient method to infer the time course of the parameter variations from time-series with short-range correlations. Importantly, this method evaluates the model evidence to objectively select between competing high-level models. We apply this method to detect anomalous price movements in financial markets, characterize cancer cell invasiveness, identify historical policies relevant for working safety in coal mines, and compare different climate change scenarios to forecast global warming. äBayesian model selection for complex dynamic systems (epmc).pdf DeepDyve Pubget Overpricing