|
gptkbp:instanceOf
|
probabilistic graphical model
|
|
gptkbp:alsoKnownAs
|
belief networks
Bayes networks
Bayesian belief networks
|
|
gptkbp:application
|
gptkb:gene_regulatory_networks
computer vision
natural language processing
robotics
bioinformatics
risk analysis
diagnosis
fault diagnosis
|
|
gptkbp:assumes
|
conditional independence
|
|
gptkbp:canBe
|
data
|
|
gptkbp:canBeSpecifiedBy
|
experts
|
|
gptkbp:composedOf
|
edges
nodes
|
|
gptkbp:edgesRepresent
|
conditional dependencies
|
|
gptkbp:extendsTo
|
gptkb:dynamic_Bayesian_networks
decision networks
influence diagrams
|
|
gptkbp:firstDescribed
|
1980s
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Bayesian Networks
|
|
gptkbp:inferenceAlgorithms
|
gptkb:Markov_Chain_Monte_Carlo
belief propagation
variable elimination
|
|
gptkbp:inferenceMethod
|
approximate inference
exact inference
|
|
gptkbp:nodesRepresent
|
random variables
|
|
gptkbp:originatedIn
|
gptkb:Judea_Pearl
|
|
gptkbp:parametersRepresent
|
conditional probability distributions
|
|
gptkbp:relatedConcept
|
probabilistic programming
graphical models
causal networks
|
|
gptkbp:relatedTo
|
gptkb:Markov_networks
causal inference
probabilistic reasoning
|
|
gptkbp:represents
|
conditional dependencies
probabilistic relationships
|
|
gptkbp:structureType
|
gptkb:directed_acyclic_graph
|
|
gptkbp:usedFor
|
inference
decision making
diagnosis
prediction
|
|
gptkbp:usedIn
|
gptkb:artificial_intelligence
gptkb:machine_learning
statistics
|
|
gptkbp:bfsParent
|
gptkb:Generative_Models
gptkb:Bayesian_Learning
gptkb:Directed_Acyclic_Graph
|
|
gptkbp:bfsLayer
|
7
|