## Model

- A finite model describing a probability distribution over all possible sequences of equal length
- “Natural” scoring function
- (Conditional) Maximum likelihood “training”

## Markov

- k-order Markov chain: current state dependent on k previous states
- The next state in a 1st-order Markov model depends on current state

## Hidden

- Hidden states generate visible symbols

## Assumptions

- Independence of states
- No long range correlation

- Independence of states

## Example: HMMgene, A. Krogh (1998), In Guide to Human Genome Computing, 261-274.

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