Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
probabilistic graphical model |
| gptkbp:alsoKnownAs |
gptkb:Bayes_network
gptkb:belief_network Boltzmann machine |
| gptkbp:appliesTo |
gptkb:dictionary
gptkb:gene_regulatory_networks risk analysis diagnosis anomaly detection decision support systems prediction data imputation |
| gptkbp:canBe |
data
expert knowledge |
| gptkbp:canLearnMove |
parameter learning algorithms
structure learning algorithms |
| gptkbp:component |
edges
nodes conditional probability tables |
| gptkbp:describedBy |
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems
|
| gptkbp:enables |
probabilistic reasoning
learning from data efficient inference |
| gptkbp:formedBy |
gptkb:Judea_Pearl
|
| gptkbp:hasModel |
uncertainty
causal relationships joint probability distributions |
| gptkbp:hasProperty |
no directed cycles
local Markov property factorization of joint distribution |
| https://www.w3.org/2000/01/rdf-schema#label |
Bayes net
|
| gptkbp:inferenceMethod |
belief propagation
variable elimination sampling methods junction tree algorithm |
| gptkbp:namedAfter |
gptkb:Thomas_Bayes
|
| gptkbp:relatedTo |
gptkb:Markov_network
gptkb:dynamic_Bayesian_network gptkb:causal_network |
| gptkbp:represents |
set of variables and their conditional dependencies
|
| gptkbp:usedIn |
gptkb:artificial_intelligence
gptkb:machine_learning statistics bioinformatics decision making diagnosis |
| gptkbp:uses |
gptkb:directed_acyclic_graph
|
| gptkbp:bfsParent |
gptkb:Bayes_network
gptkb:Bayesian_belief_network |
| gptkbp:bfsLayer |
6
|