Small Introduction to Markov Chains

I like the visualization vicapow did on Markov chains a while ago as well.<p>&quot;Markov chains explained visually&quot;: <a href="http://setosa.io/blog/2014/07/26/markov-chains/" rel="nofollow">http:&#x2F;&#x2F;setosa.io&#x2F;blog&#x2F;2014&#x2F;07&#x2F;26&#x2F;markov-chains&#x2F;</a>

That is a great intro. Thanks for putting it together. If you were to continue this to a Part #2, I would be interested in seeing the approach for determining if something was &quot;abnormal&quot;. Is it considered abnormal to add a node to the chain, or to add a transition? Is detecting abnormal behavior more complicated than detecting new nodes or transitions?

Awesome intro! I&#x27;ve used Markov Chain libraries before but never took the time to try and understand them. This makes a lot of sense.<p>Are there different or more efficient probability calculations that can be done other than the provided algorithm? It seems somewhat simple.

damn, that&#x27;s some tiny &lt;pre&gt; font-size.