Bayesian networks

GPTKB entity

Statements (52)
Predicate Object
gptkbp:instanceOf probabilistic graphical model
gptkbp:alsoKnownAs gptkb:Bayes_nets
belief networks
gptkbp:appliesTo gptkb:artificial_intelligence
computer vision
information retrieval
speech recognition
bioinformatics
risk analysis
diagnosis
gptkbp:component conditional probability tables
edges (conditional dependencies)
nodes (random variables)
https://www.w3.org/2000/01/rdf-schema#label Bayesian networks
gptkbp:introducedIn 1980s
gptkbp:mathematicalFoundation gptkb:Bayes'_theorem
gptkbp:originatedIn gptkb:Judea_Pearl
gptkbp:property can be constructed manually or learned from data
can be extended to dynamic Bayesian networks
can be extended to influence diagrams
can be used for anomaly detection
can be used for causal discovery
can be used for data fusion
can be used for decision support
can be used for knowledge representation
can be used for reasoning under uncertainty
can be used for time series analysis
can encode conditional independence
can handle missing data
can represent joint probability distributions
support efficient inference algorithms
support exact and approximate inference
support learning from data
support parameter learning
support structure learning
gptkbp:relatedTo gptkb:dynamic_Bayesian_networks
gptkb:hidden_Markov_models
gptkb:Markov_networks
causal networks
gptkbp:represents set of variables and their conditional dependencies
gptkbp:usedFor gptkb:machine_learning
decision making
diagnosis
probabilistic inference
prediction
causal reasoning
gptkbp:uses gptkb:directed_acyclic_graph
gptkbp:bfsParent gptkb:Judea_Pearl
gptkb:directed_acyclic_graph
gptkb:Michael_A._Jordan
gptkb:Probabilistic_logic
gptkbp:bfsLayer 5