Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:abbreviation |
gptkb:HMM
|
| gptkbp:alternativeTo |
gptkb:dynamic_Bayesian_network
recurrent neural network conditional random field |
| gptkbp:application |
financial modeling
handwriting recognition activity recognition gene prediction part-of-speech tagging |
| gptkbp:canBe |
continuous
discrete |
| gptkbp:category |
time series analysis
probabilistic graphical models sequence modeling |
| gptkbp:describes |
systems with unobservable (hidden) states
|
| gptkbp:fileExtension |
gptkb:factorial_hidden_Markov_model
gptkb:hidden_semi-Markov_model gptkb:input-output_hidden_Markov_model |
| gptkbp:generalizes |
Markov chain
|
| gptkbp:hasComponent |
transition probabilities
emission probabilities hidden states initial state distribution observable states |
| https://www.w3.org/2000/01/rdf-schema#label |
Hidden Markov model
|
| gptkbp:introducedIn |
1966
|
| gptkbp:inventedBy |
gptkb:Leonard_E._Baum
gptkb:T._Petrie |
| gptkbp:limitation |
assumes Markov property
difficulty with long-range dependencies limited to first-order dependencies |
| gptkbp:mathematicalFoundation |
gptkb:probability_theory
stochastic processes |
| gptkbp:parameter |
transition matrix
emission matrix initial probability vector number of hidden states |
| gptkbp:relatedTo |
gptkb:Baum–Welch_algorithm
gptkb:Viterbi_algorithm Markov chain Backward algorithm Forward algorithm |
| gptkbp:usedIn |
gptkb:machine_learning
natural language processing speech recognition bioinformatics pattern recognition |
| gptkbp:bfsParent |
gptkb:Particle_filter
|
| gptkbp:bfsLayer |
5
|