Click or drag to resize

BayesServer.Learning.Parameters Namespace

Provides parameter learning for Bayesian networks and Dynamic Bayesian networks. See Learn for sample code.
Public classDistributedMapperContext
Contains information used during distributed parameter learning.
Public classDistributerContext
Contains contextual information about the process/iteration being distributed.
Public classDistributionSpecification
Identifies a node's distribution to learn, and options for learning.
Public classInitializationOptions
Options governing the initialization of distributions at the start of parameter learning.
Public classOnlineLearning
Adapts the parameters of a Bayesian network, using Bayesian statistics.
Public classOnlineLearningOptions
Options for online learning (adaptation using Bayesian statistics).
Public classParameterLearning
Learns the parameters of Bayesian networks and Dynamic Bayesian networks, from data. See [M:BayesServer.Learning.Parameters.ParameterLearning.Learn(IEvidenceReader, ParameterLearningOptions)] for sample code.
Public classParameterLearningOptions
Options governing parameter learning.
Public classParameterLearningOutput
Contains summary information returned by Learn.
Public classParameterLearningProgressInfo
Provides progress information during Learn.
Public classPriors
Contains parameters used to avoid boundary conditions during learning.
Public interfaceIParameterLearningProgress
Interface to provide progress information during parameter learning.
Public enumerationConvergenceMethod
The method used to determine whether learning has converged.
Public enumerationDecisionPostProcessingMethod
The type of post processing to be applied to the distributions of decision nodes at the end of parameter learning.
Public enumerationDiscretePriorMethod
The type of discrete prior to use for discrete distributions during parameter learning.
Public enumerationDistributionMonitoring
Indicates which distribution to monitor during learning.
Public enumerationInitializationMethod
Determines the algorithm used to initialize distributions during parameter learning.
Public enumerationTimeSeriesMode
Determines how time series distributions are learned.