R Tutorial - Bayesian statistics
http://www.r-tutor.com/taxonomy/term/300/all
enHierarchical Linear Model
http://www.r-tutor.com/gpu-computing/rbayes/rhierlmc
<!--l. 32--><p class="noindent" >
<a href="/gpu-computing/rbayes/rhierlmc"><img src="fractal-01h.jpeg" alt=" " class="float-left" /></a>
<a
href="/elementary-statistics/multiple-linear-regression" >Linear regression</a> probably is the most familiar technique in data analysis, but its
application is often hamstrung by model assumptions. For instance, if the data has a
hierarchical structure, quite often the assumptions of linear regression are feasible
only at local levels. We will investigate an extension of the linear model to bi-level
hierarchies.
</p>
<p><a href="http://www.r-tutor.com/gpu-computing/rbayes/rhierlmc" target="_blank">read more</a></p>http://www.r-tutor.com/gpu-computing/rbayes/rhierlmc#commentsGPU Computing with RBayesian regressionBayesian statisticshierarchical linear modellinear regressionMarkov Chain Monte CarloMCMCcbindexplengthlevelslistlogmeanrhierLinearModelstrbayesmcodacudaBayesregrpudcheeseMon, 22 Jul 2013 19:39:44 +0000rtutor.chiyau160 at http://www.r-tutor.comBayesian Classification with Gaussian Process
http://www.r-tutor.com/gpu-computing/gaussian-process/rvbm
<!--l. 30--><p class="noindent" >
<a href="/gpu-computing/gaussian-process/rvbm"><img src="fractal-09h.jpeg" alt=" " class="float-left" /></a>
Despite prowess of the <a
href="/gpu-computing/svm/rpusvm-1" >support vector machine</a>, it is not specifically designed to
extract features relevant to the prediction. For example, in network intrusion
detection, we need to learn relevant network statistics for the network defense. In
consumer credit rating, we would like to determine relevant financial records for the
credit score. As for medical genetics research, we aim to identify genes relevant to the
illness.
</p>
<p><a href="http://www.r-tutor.com/gpu-computing/gaussian-process/rvbm" target="_blank">read more</a></p>http://www.r-tutor.com/gpu-computing/gaussian-process/rvbm#commentsGPU Computing with RBayesian classificationBayesian statisticsclassificationgaussian processMarkov Chain Monte CarloMCMCexprsplotpointsrvbmsummaryvbmpBiobaserpudvbmpBRCA12rvbm.sample.testrvbm.sample.trainSun, 06 Jan 2013 19:14:04 +0000rtutor.chiyau159 at http://www.r-tutor.comBayesian Inference Using OpenBUGS
http://www.r-tutor.com/bayesian-statistics/openbugs
<p class="noindent" >
<a href="/bayesian-statistics/openbugs"><img src="fractal-04h.jpeg" alt=" " class="float-left" /></a>
In our previous statistics tutorials, we have treated population parameters as fixed
values, and provided point estimates and confidence intervals for them. An
alternative approach is the Bayesian statistics. It treats population parameters as
random variables. Probability becomes a measure of our belief in possible outcomes.
With new tools like OpenBUGS, tackling new problems requires building new
models, instead of creating yet another R command.
</p>
<p><a href="http://www.r-tutor.com/bayesian-statistics/openbugs" target="_blank">read more</a></p>http://www.r-tutor.com/bayesian-statistics/openbugs#commentsBayesian Inference Using OpenBUGSBayesian statisticsbeta distributionbinomial distributionMarkov Chain Monte CarloMCMCacfplotbugsdensityplotfile.pathgelman.diaggelman.plotread.bugstablexyplotcodaR2OpenBUGSsurveyMon, 23 Jul 2012 01:31:57 +0000rtutor.chiyau158 at http://www.r-tutor.com