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10.1080/10618600.2016.1200472 http://scihub22266oqcxt.onion/10.1080/10618600.2016.1200472 C5455957!5455957 !28626349     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28626349 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28626349 .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 2017 ; 26 (2 ): 285-295 Nephropedia Template TP {{tp|p=28626349 |t=2017. Identifying Mixtures of Mixtures Using Bayesian Estimation |pdf=|usr=}}{{28626349 }} gab.com Text Identifying Mixtures of Mixtures Using Bayesian Estimation JO: J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=28626349 #FOAvip The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior, where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows us to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows us to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semiparametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark datasets. Supplementary materials for this article are available online. Twit Text FOAVip Identifying Mixtures of Mixtures Using Bayesian Estimation ????J Comput Graph Stat http://www.kidney.de/pget.php?&tw=1&pmid=28626349 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28626349 #FOAvip English Wikipedia <ref>{{Cite pmid|28626349 }}</ref> Identifying Mixtures of Mixtures Using Bayesian Estimation #MMPMID28626349 Malsiner-Walli G ; Frühwirth-Schnatter S ; Grün B J Comput Graph Stat 2017[Apr]; 26 (2 ): 285-295 PMID28626349 show ga The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior, where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows us to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows us to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semiparametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark datasets. Supplementary materials for this article are available online. äIdentifying Mixtures of Mixtures Using Bayesian Estimation (epmc).pdf DeepDyve Pubget Overpricing