Hidden Markov Model | Clearly Explained

First described by Andrey Andreyevich Markov in 1877, Markov Chain and Markov Process have been one of the most famous method in the study of Stochastic process. Markov Process is a type of stochastic model that assume the current observation relies only on the previous events with a certain probability and can be visualized as a Markov Chain.

The Hidden Markov Model add onto the original Markov Model with the assumption that another "Hidden" state are present in the system that have direct consequences to the outcome of the current events. With that, this model have been successfully implement into many research field including Bioinformatics, Artificial Intelligent and many more

With that, I hope that this video can be a non-mathematical introduction to people wanting to understand the concept but do not need to know the exact calculation involve in the matrix multiplication of the modeling process, or maybe want to learnt about those later.

Please do leave a comment if you have any question :)

Slides： http://bit.ly/Brandon_Yeo

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