Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:application |
gptkb:Gene_prediction
Machine translation Handwriting recognition Financial modeling Part-of-speech tagging |
| gptkbp:assumes |
gptkb:Markov_property
Observations are probabilistic functions of states |
| gptkbp:canBe |
Continuous
Discrete |
| gptkbp:canBeTrainedBy |
Expectation-maximization
|
| gptkbp:decodingAlgorithm |
gptkb:Viterbi_algorithm
|
| gptkbp:generalizes |
Markov chain
|
| gptkbp:hasComponent |
Transition probabilities
Emission probabilities Hidden states Initial state distribution Observable states |
| gptkbp:hasObservationSpace |
Continuous
Finite |
| gptkbp:hasStateSpace |
Finite
|
| https://www.w3.org/2000/01/rdf-schema#label |
Hidden Markov Models
|
| gptkbp:inferenceAlgorithm |
Forward-backward algorithm
|
| gptkbp:input |
Sequence of states
|
| gptkbp:limitation |
Assumes independence of observations given state
Limited to first-order Markov property |
| gptkbp:mathematicalFoundation |
gptkb:Probability_theory
gptkb:Linear_algebra Statistics |
| gptkbp:output |
Sequence of observations
|
| gptkbp:parameter |
gptkb:Baum-Welch_algorithm
|
| gptkbp:proposedBy |
gptkb:Leonard_E._Baum
1966 |
| gptkbp:relatedTo |
gptkb:Dynamic_Bayesian_network
gptkb:Baum-Welch_algorithm gptkb:Viterbi_algorithm Markov chain Forward-backward algorithm |
| gptkbp:type |
Generative model
|
| gptkbp:usedFor |
Sequence modeling
Stochastic modeling Temporal pattern recognition |
| gptkbp:usedIn |
gptkb:Bioinformatics
Natural language processing Time series analysis Speech recognition Pattern recognition |
| gptkbp:visualizes |
State diagram
|
| gptkbp:bfsParent |
gptkb:Speech_Recognition
|
| gptkbp:bfsLayer |
6
|