The Hidden Markov Model demystified
Over the past few months I've been using LinkedIn more and more, as a way finding great articles and tutorials about all things data science and machine learning. While I'm a big fan of Twitter, LinkedIn appears to be a better platform for discovering tech related articles, written or suggested by fellow astronomers and data scientists. A few days ago Elodie Thilliez's blog post The Hidden Markov Model demystified (Part 1) appeared in my feed. This was a really nice surprise. I haven't seen Elodie since we both left Swinburne. After finishing her PhD last year, she moved straight into data science role at the Deakin Software & Technology Innovation Lab – DSTIL (formerly the Swinburne Software Innovation Lab – SSIL) and I was curious to know what she had been up to.
The Hidden Markov Model (HMM) is a statistical Markov model in which the system being modelled is assumed to be a Markov process with unobserved (i.e. hidden) states. The Hidden Markov Model (HMM) Wikipedia page has a good example of what HMM is and how it works.
In The Hidden Markov Model demystified (Part 1 ) Elodie talks about how HMM is used and illustrates the process with a really simple example. Her follow up blog post, The Hidden Markov Model demystified (Part 2 ) – published today – talks about the mathematics, specifically the probabilities involved in the Forward Algorithm, and how to Implement HMM in R.