Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Boltzmann machine
probabilistic graphical model |
| gptkbp:canBe |
continuous
discrete hybrid uncertainty missing data fully observable partially observable |
| gptkbp:canLearnMove |
gptkb:Markov_Chain_Monte_Carlo
variational inference expectation-maximization |
| gptkbp:capturedBy |
temporal evolution
|
| gptkbp:component |
edges
nodes conditional probability distributions |
| gptkbp:extendsTo |
Boltzmann machine
|
| gptkbp:field |
gptkb:artificial_intelligence
gptkb:machine_learning data science statistics |
| gptkbp:generalizes |
gptkb:Kalman_filter
gptkb:Hidden_Markov_Model |
| gptkbp:hasApplication |
natural language processing
financial modeling activity recognition fault diagnosis gene regulatory network modeling |
| gptkbp:hasModel |
nonlinear dynamics
non-Gaussian noise sequences of variables |
| https://www.w3.org/2000/01/rdf-schema#label |
Dynamic Bayesian Network
|
| gptkbp:introduced |
gptkb:Kevin_Murphy
|
| gptkbp:introducedIn |
1998
|
| gptkbp:parameter |
initial state distribution
observation model transition model |
| gptkbp:relatedTo |
gptkb:Kalman_filter
gptkb:Hidden_Markov_Model |
| gptkbp:represents |
Markov processes
conditional dependencies over time non-Markovian processes |
| gptkbp:usedFor |
robotics
speech recognition time series analysis bioinformatics modeling temporal processes |
| gptkbp:visualizes |
two-slice temporal Bayes net
|
| gptkbp:bfsParent |
gptkb:Bayesian_Network
|
| gptkbp:bfsLayer |
7
|