Tag viterbi

Hidden Markov Models

Archit Vora 1 Comment

From a Clustering Perspective

This section summarizes a lecture from the University of Washington [0] on clustering time series data, considering the significance of both the data and indices.

Other potential applications include:

  1. Honey bee dance: Bees switch from one dance to another to convey messages.
  2. Conference conversations: Segmenting speaker assignments based on the spoken turns.
  3. Gym exercises: Identifying exercises from pulse rate data as people switch between activities.

Model

The following screenshots are from a YouTube video [1] by the Mathematical Monk, illustrating the model:

Suppose you’re developing handwriting recognition and need to recognize a hidden variable.

The prediction for “i” depends solely on the previous character being “h,” disregarding how “h” was written.

hmm_concept
hmm_parameters

Code and Notebook

You can find the code and notebook at the following GitHub link [2], which extensively explains:

  1. The structure of the model.
  2. The forward algorithm for calculating the likelihood of a given observation.
  3. The backward algorithm for finding the most probable state sequence given an observation (also known as decoding).
  4. The forward-backward algorithm for inferring model parameters from a set of observed sequences.

References

[0] : https://www.coursera.org/learn/ml-clustering-and-retrieval/home/welcome

[1] : https://www.youtube.com/watch?v=TPRoLreU9lA

[2] : https://github.com/arcarchit/datastories/blob/master/hmm.ipynb

[3] : https://web.stanford.edu/~jurafsky/slp3/A.pdf