I work at the intersection of data science, technology and human-centred design. Former astrophysicist & e-Research/data consultant.
 
The Hidden Markov Model demystified

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.

 

.Astronomy9 Day Zero Planning

Friday morning coffee with the folks at Silverpond

Friday morning coffee with the folks at Silverpond