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<?xml-stylesheet type="text/xsl" href="../assets/xml/rss.xsl" media="all"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bounded Rationality (Posts about Poisson)</title><link>http://bjlkeng.github.io/</link><description></description><atom:link href="http://bjlkeng.github.io/categories/poisson.xml" rel="self" type="application/rss+xml"></atom:link><language>en</language><lastBuildDate>Tue, 10 Mar 2026 20:54:59 GMT</lastBuildDate><generator>Nikola (getnikola.com)</generator><docs>http://blogs.law.harvard.edu/tech/rss</docs><item><title>A Probabilistic View of Linear Regression</title><link>http://bjlkeng.github.io/posts/a-probabilistic-view-of-regression/</link><dc:creator>Brian Keng</dc:creator><description>&lt;div&gt;&lt;p&gt;One thing that I always disliked about introductory material to linear
regression is how randomness is explained.  The explanations always
seemed unintuitive because, as I have frequently seen it, they appear as an
after thought rather than the central focus of the model.
In this post, I'm going to try to
take another approach to building an ordinary linear regression model starting
from a probabilistic point of view (which is pretty much just a Bayesian view).
After the general idea is established, I'll modify the model a bit and end up
with a Poisson regression using the exact same principles showing how
generalized linear models aren't any more complicated.  Hopefully, this will
help explain the "randomness" in linear regression in a more intuitive way.&lt;/p&gt;
&lt;p&gt;&lt;a href="http://bjlkeng.github.io/posts/a-probabilistic-view-of-regression/"&gt;Read more…&lt;/a&gt; (12 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>Bayesian</category><category>logistic</category><category>mathjax</category><category>Poisson</category><category>probability</category><category>regression</category><guid>http://bjlkeng.github.io/posts/a-probabilistic-view-of-regression/</guid><pubDate>Sun, 15 May 2016 00:43:05 GMT</pubDate></item></channel></rss>