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10.1080/01621459.2014.896806 http://scihub22266oqcxt.onion/10.1080/01621459.2014.896806 C4465207!4465207!26078481     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26078481&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 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Stat+Assoc 2015 ; 110 (509): 159-74 Nephropedia Template TP {{tp|p=26078481|t=2015. Bayesian Inference of Multiple Gaussian Graphical Models |pdf=|usr=}}{{26078481}} gab.com Text Bayesian Inference of Multiple Gaussian Graphical Models JO: J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=26078481 #FOAvip In this paper, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions. Twit Text FOAVip Bayesian Inference of Multiple Gaussian Graphical Models ????J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=26078481 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26078481 #FOAvip English Wikipedia <ref>{{Cite pmid|26078481}}</ref> Bayesian Inference of Multiple Gaussian Graphical Models #MMPMID26078481 Peterson CB; Stingo FC; Vannucci M J Am Stat Assoc 2015[Mar]; 110 (509): 159-74 PMID26078481show ga In this paper, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions. äBayesian Inference of Multiple Gaussian Graphical Models (epmc).pdf DeepDyve Pubget Overpricing