0x02. Hidden Markov Models
This project is about implementing hidden markov models.
- What is the Markov property?
- What is a Markov chain?
- What is a state?
- What is a transition probability/matrix?
- What is a stationary state?
- What is a regular Markov chain?
- How to determine if a transition matrix is regular
- What is an absorbing state?
- What is a transient state?
- What is a recurrent state?
- What is an absorbing Markov chain?
- What is a Hidden Markov Model?
- What is a hidden state?
- What is an observation?
- What is an emission probability/matrix?
- What is a Trellis diagram?
- What is the Forward algorithm and how do you implement it?
- What is decoding?
- What is the Viterbi algorithm and how do you implement it?
- What is the Forward-Backward algorithm and how do you implement it?
- What is the Baum-Welch algorithm and how do you implement it?
| File | Description |
|---|---|
| 0-markov_chain.py | Determines the probability of a markov chain being in a particular state after a specified number of iterations. |
| 1-regular.py | Determines the steady state probabilities of a regular markov chain. |
| 2-absorbing.py | Determines if a markov chain is absorbing. |
| 3-forward.py | Performs the forward algorithm for a hidden markov model. |
| 4-viterbi.py | Calculates the most likely sequence of hidden states for a hidden markov model. |
| 5-backward.py | Performs the backward algorithm for a hidden markov model. |
| 6-baum_welch.py | Performs the Baum-Welch algorithm for a hidden markov model. |