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10.1111/rssb.12080 http://scihub22266oqcxt.onion/10.1111/rssb.12080 C4467036!4467036 !26089740     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26089740 &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       J+R+Stat+Soc+Series+B+Stat+Methodol 2015 ; 77 (3 ): 617-646 Nephropedia Template TP {{tp|p=26089740 |t=2015. Frequentist accuracy of Bayesian estimates |pdf=|usr=}}{{26089740 }} gab.com Text Frequentist accuracy of Bayesian estimates JO: J R Stat Soc Series B Stat Methodol http://www.kidney.de/pget.php?&tw=1&pmid=26089740 #FOAvip In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of "uninformative" prior distribution intended to have minimal inferential influence. Bayes rule will still produce nice-looking estimates and credible intervals, but these lack the logical force attached to experience-based priors and require further justification. This paper concerns the frequentist assessment of Bayes estimates. A simple formula is shown to give the frequentist standard deviation of a Bayesian point estimate. The same simulations required for the point estimate also produce the standard deviation. Exponential family models make the calculations particularly simple, and bring in a connection to the parametric bootstrap. Twit Text FOAVip Frequentist accuracy of Bayesian estimates ????J R Stat Soc Series B Stat Methodol http://www.kidney.de/pget.php?&tw=1&pmid=26089740 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26089740 #FOAvip English Wikipedia <ref>{{Cite pmid|26089740 }}</ref> Frequentist accuracy of Bayesian estimates #MMPMID26089740 Efron B J R Stat Soc Series B Stat Methodol 2015[Jun]; 77 (3 ): 617-646 PMID26089740 show ga In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of "uninformative" prior distribution intended to have minimal inferential influence. Bayes rule will still produce nice-looking estimates and credible intervals, but these lack the logical force attached to experience-based priors and require further justification. This paper concerns the frequentist assessment of Bayes estimates. A simple formula is shown to give the frequentist standard deviation of a Bayesian point estimate. The same simulations required for the point estimate also produce the standard deviation. Exponential family models make the calculations particularly simple, and bring in a connection to the parametric bootstrap. ?Frequentist accuracy of Bayesian estimates (epmc).pdf DeepDyve Pubget Overpricing