Build a Hidden Markov Model in PyTorch

Use PyTorch to Build a Hidden Markov Model for both Weather Prediction and whether a person is Healthy or Feverish.

PyTorch is a deep learning neural networks package for Python [Youtube - PyTorch Explained].

This is a practical project for learning Probabilistic Graphical Models (PGM). It implements the Viterbi, Forward-Backward and the Baum Welch algorithms.

I added my own personal enhancements (like clearer code documentation) and modifications to this original project put together by TreB1eN.

Hidden Markov Model (HMM)

A Markov chain is useful when we need to compute a probability for a sequence of observable events. In many cases, however, the events we are interested in are hidden: we don’t observe them directly. For example we don’t normally observe part-of-speech tags in a text. Rather, we see words, and must infer the tags from the word sequence. [pg 2, Dan Jurafsky, Stanford]

In HMM the sequences are hidden because it is not possible to tell the state merely by the output symbol.