Uses of Package
com.bayesserver.learning.parameters

Packages that use com.bayesserver.learning.parameters

Package	Description
com.bayesserver.learning.parameters

Classes in com.bayesserver.learning.parameters used by com.bayesserver.learning.parameters

Class and 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.
DistributedMapperContext Contains information used during distributed parameter learning.
DistributerContext Contains contextual information about the process/iteration being distributed.
DistributionMonitoring Indicates which distribution to monitor during learning.
DistributionSpecification Identifies a node's distribution to learn, and options for learning.
InitializationMethod Determines the algorithm used to initialize distributions during parameter learning.
InitializationOptions Options governing the initialization of distributions at the start of parameter learning.
OnlineLearningOptions Options for online learning (adaptation using Bayesian statistics).
ParameterLearningOptions Options governing parameter learning.
ParameterLearningOutput Contains summary information returned by ParameterLearning.learn(com.bayesserver.data.EvidenceReaderCommand, com.bayesserver.learning.parameters.ParameterLearningOptions).
ParameterLearningProgress Interface to provide progress information during parameter learning.
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.
TimeSeriesMode Determines how time series distributions are learned.