HMM

A HMM is a Hidden Markov Model. Markov models are often used to model observation sequences. The fundamental assumption in a Markov model is that the probability of an observation (event) can only depend on the previous observation. A HMM can be visualised as a graph with a number of states. If two states are connected, the graph has a line that connects them. The following picture shows a HMM with two states, labeled "Rainy" and "Sunny". Each state can emit three symbols (these are not visible in the graph).

For an introduction into HMM's see Rabiner (1989).

### Links to this page

- HMM & HMM & HMMObservationSequence: Get cross-entropy
- HMM & HMM: Get cross-entropy...
- HMM & HMMObservationSequence: Get cross-entropy
- HMM & HMMObservationSequence: Get probability
- HMM & HMMObservationSequence: To TableOfReal (bigrams)...
- HMM & HMMObservationSequences: Learn...
- HMM & HMMStateSequence: Get probability
- HMM: Create simple HMM...
- HMM: Extract emission probabilities
- HMM: Extract transition probabilities
- HMM: Get emission probability...
- HMM: Get expected duration in state...
- HMM: Get p (time, state)...
- HMM: Get p (time, state, symbol)...
- HMM: Get probability staying in state...
- HMM: Get start probability...
- HMM: Get transition probability...
- HMM: To HMMObservationSequence...
- HMMObservationSequence
- HMMStateSequence