| ConvergenceMethod |
The method used to determine whether learning has converged.
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| DecisionPostProcessingMethod |
The type of post processing to be applied to the distributions of decision nodes at the end of parameter learning.
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| DiscretePriorMethod |
The type of discrete prior to use for discrete distributions during parameter learning.
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| DistributedMapperContext |
Contains information used during distributed parameter learning.
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| DistributerContext |
Contains contextual information about the process/iteration being distributed.
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| DistributionMonitoring |
Indicates which distribution to monitor during learning.
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| DistributionSpecification |
Identifies a node's distribution to learn, and options for learning.
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| InitializationMethod |
Determines the algorithm used to initialize distributions during parameter learning.
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| InitializationOptions |
Options governing the initialization of distributions at the start of parameter learning.
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| OnlineLearningOptions |
Options for online learning (adaptation using Bayesian statistics).
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| ParameterLearningOptions |
Options governing parameter learning.
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| ParameterLearningOutput |
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| ParameterLearningProgress |
Interface to provide progress information during parameter learning.
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| ParameterLearningProgressInfo |
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| Priors |
Contains parameters used to avoid boundary conditions during learning.
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| TimeSeriesMode |
Determines how time series distributions are learned.
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