<story><title>Interactive Visualization of Gaussian Processes</title><url>http://www.infinitecuriosity.org/vizgp/</url></story><parent_chain></parent_chain><comment><author>trombonechamp</author><text>In case anyone else wants to know how the Gaussian process is animated, here are the two links provided in the code:&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;math.stackexchange.com&amp;#x2F;a&amp;#x2F;1930880&amp;#x2F;901583&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;math.stackexchange.com&amp;#x2F;a&amp;#x2F;1930880&amp;#x2F;901583&lt;/a&gt;&lt;p&gt;&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;mlss.tuebingen.mpg.de&amp;#x2F;2013&amp;#x2F;Hennig_2013_Animating_Samples_from_Gaussian_Distributions.pdf&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;mlss.tuebingen.mpg.de&amp;#x2F;2013&amp;#x2F;Hennig_2013_Animating_Samp...&lt;/a&gt;</text></comment>
<story><title>Interactive Visualization of Gaussian Processes</title><url>http://www.infinitecuriosity.org/vizgp/</url></story><parent_chain></parent_chain><comment><author>clircle</author><text>This is very nice. Gaussian process regression didn&amp;#x27;t click for me until I thought of the data as a partially observed sample of size one from a stochastic process.&lt;p&gt;Practically, I have a hard time using Gaussian process regression. I find regression with splines to be reasonable and fast, and I don&amp;#x27;t have to fret about the nugget parameter or the covariance function structure. But I admit GP regression has a beautiful theory.&lt;p&gt;But there is an equivalence between (smoothing) splines and certain types of Gaussian process models. [1]&lt;p&gt;[1] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;pages.stat.wisc.edu&amp;#x2F;~wahba&amp;#x2F;ftp1&amp;#x2F;oldie&amp;#x2F;kw70bayes.pdf&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;pages.stat.wisc.edu&amp;#x2F;~wahba&amp;#x2F;ftp1&amp;#x2F;oldie&amp;#x2F;kw70bayes.pdf&lt;/a&gt;</text></comment>