5 ms·
Small Introduction to Markov Chains
- jszymborski 12y agodamn, that's some tiny <pre> font-size.
- taterbase 12y agoAwesome intro! I've used Markov Chain libraries before but never took the time to try and understand them. This makes a lot of sense. Are there different or more efficient probability calculations that can be done other than the provided algorithm? It seems somewhat simple.
- tantalum 12y agoYes there are more complex probabilities that you can calculate. For example you can calculate the probability of reaching some state that is one or more transitions away given a starting state. You can find more info on some of other calculations you can do on Wikipedia.
- sswaner 12y agoThat 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 "abnormal". 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?
- tantalum 12y agoAbnormal really means that it has a probability less than some threshold. So you might have a transition that has a probability of less then 0.1, depending on your situation, you might consider that abnormal even thought that transition does exist. Also a non existent transition would be considered "abnormal" because the probability is 0 in your model. I've been thinking about doing a part 2 with text analysis and a cool use case would be to detect "abnormal" text from an author because he/she uses strings of words that have a low probability in the rest of their text. Hope this helps and I'm glad you like the post.
- sswaner 12y agoThanks for the response. Yours is the clearest intro to Markov Chains that I have found.
- mts_ 12y agoI like the visualization vicapow did on Markov chains a while ago as well. "Markov chains explained visually": http://setosa.io/blog/2014/07/26/markov-chains/ http://setosa.io/blog/2014/07/26/markov-chains/