Probability propagation in graphical independence networks, also known as Bayesian networks or probabilistic expert systems.
|Author||Søren Højsgaard <firstname.lastname@example.org>|
|Date of publication||2016-10-17 11:09:28|
|Maintainer||Søren Højsgaard <email@example.com>|
|License||GPL (>= 2)|
compile-cpt: Compile conditional probability tables / cliques potentials.
cptable: Create conditional probability tables (CPTs)
evidence-object: Evidence objects
extract-cpt: Extract conditional probabilities and clique potentials from...
finding: Set, retrieve, and retract finding in Bayesian network.
grain-compile: Compile a graphical independence network (a Bayesian network)
grain-evi: Set evidence in grain objects
grain-evidence: Set evidence.
grain-generics: gRain generics
grain-main: Graphical Independence Network
grain-predict: Make predictions from a probabilistic network
grain-propagate: Propagate a graphical independence network (a Bayesian...
grain-simulate: Simulate from an independence network
internal-gRain: Internal functions for the gRain package
load-save-hugin: Load and save Hugin net files
logical: Conditional probability tables based on logical dependencies
mendel: Mendelian segregation
querygrain: Query a network
repeatPattern: Create repeated patterns in Bayesian networks
set-jevidence: Set joint evidence in grain objects
update.CPTgrain: Update a Bayesian network