Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
probabilistic graphical model
|
| gptkbp:alsoKnownAs |
gptkb:belief_network
gptkb:Bayes_net Boltzmann machine |
| gptkbp:application |
gptkb:gene_regulatory_networks
information retrieval robotics speech recognition bioinformatics image processing risk analysis diagnosis fraud detection forecasting |
| gptkbp:canBe |
learned from data
specified by experts |
| gptkbp:component |
edges
nodes conditional probability tables |
| gptkbp:describes |
joint probability distribution
|
| gptkbp:field |
gptkb:artificial_intelligence
computer science data science statistics |
| https://www.w3.org/2000/01/rdf-schema#label |
Bayes network
|
| gptkbp:inventedBy |
gptkb:Judea_Pearl
|
| gptkbp:limitation |
cannot represent cyclic dependencies
complexity increases with number of variables |
| gptkbp:namedAfter |
gptkb:Thomas_Bayes
|
| gptkbp:property |
graphical model
acyclic encodes conditional independence |
| gptkbp:relatedTo |
gptkb:Markov_network
causal inference probabilistic reasoning |
| gptkbp:represents |
set of variables and their conditional dependencies
|
| gptkbp:standardizedBy |
gptkb:XMLBIF_format
BIF format |
| gptkbp:supportsAlgorithm |
gptkb:Markov_Chain_Monte_Carlo
belief propagation expectation-maximization variable elimination junction tree algorithm |
| gptkbp:usedFor |
gptkb:machine_learning
decision making diagnosis probabilistic inference |
| gptkbp:uses |
gptkb:directed_acyclic_graph
|
| gptkbp:bfsParent |
gptkb:Boltzmann_machine
|
| gptkbp:bfsLayer |
5
|