Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
probabilistic graphical model
|
| gptkbp:alsoKnownAs |
gptkb:Bayes_network
gptkb:Bayesian_belief_network gptkb:belief_network |
| gptkbp:canBe |
learned from data
constructed by experts |
| gptkbp:component |
edges
nodes conditional probability tables |
| gptkbp:enables |
efficient computation of joint probabilities
explanation of reasoning process graphical representation of dependencies visualization of probabilistic models |
| gptkbp:field |
gptkb:mathematics
computer science data science engineering epidemiology finance genetics natural language processing robotics bioinformatics forensic science risk analysis diagnosis |
| gptkbp:hasModel |
uncertainty
causal relationships |
| https://www.w3.org/2000/01/rdf-schema#label |
Bayesian Network
|
| gptkbp:introducedIn |
1980s
|
| gptkbp:inventedBy |
gptkb:Judea_Pearl
|
| gptkbp:relatedTo |
gptkb:Markov_network
gptkb:Hidden_Markov_Model gptkb:Dynamic_Bayesian_Network gptkb:Naive_Bayes_classifier probabilistic reasoning |
| gptkbp:represents |
set of variables and their conditional dependencies
|
| gptkbp:requires |
conditional independence assumptions
|
| gptkbp:usedFor |
decision making
diagnosis probabilistic inference prediction causal reasoning |
| gptkbp:usedIn |
gptkb:artificial_intelligence
gptkb:machine_learning statistics |
| gptkbp:uses |
gptkb:directed_acyclic_graph
|
| gptkbp:bfsParent |
gptkb:Markov_Random_Field
|
| gptkbp:bfsLayer |
6
|