Package com.bayesserver.learning.parameters

Interface Summary

Interface	Description
ParameterLearningProgress	Interface to provide progress information during parameter learning.

Class Summary

Class	Description
DistributedMapperContext	Contains information used during distributed parameter learning.
DistributerContext	Contains contextual information about the process/iteration being distributed.
DistributionSpecification	Identifies a node's distribution to learn, and options for learning.
InitializationOptions	Options governing the initialization of distributions at the start of parameter learning.
OnlineLearning	Adapts the parameters of a Bayesian network, using Bayesian statistics.
OnlineLearningOptions	Options for online learning (adaptation using Bayesian statistics).
ParameterLearning	Learns the parameters of Bayesian networks and Dynamic Bayesian networks, from data.
ParameterLearningOptions	Options governing parameter learning.
ParameterLearningOutput	Contains summary information returned by ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions).
ParameterLearningProgressInfo	Provides progress information during ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions).
Priors	Contains parameters used to avoid boundary conditions during learning.

Enum Summary

Enum	Description
ConvergenceMethod	The method used to determine whether learning has converged.
DecisionPostProcessingMethod	The type of post processing to be applied to the distributions of decision nodes at the end of parameter learning.
DiscretePriorMethod	The type of discrete prior to use for discrete distributions during parameter learning.
DistributionMonitoring	Indicates which distribution to monitor during learning.
InitializationMethod	Determines the algorithm used to initialize distributions during parameter learning.
TimeSeriesMode	Determines how time series distributions are learned.