StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.StatisticalModels can be further differentiated into TrainableStatisticalModels,
which can be learned from a single input DataSet, and DifferentiableStatisticalModels,
which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.See: Description
| Interface | Description |
|---|---|
| StatisticalModel |
This interface declares methods of a statistical model, i.e., a
SequenceScore that defines a proper likelihood
over the input Sequences. |
StatisticalModels, which can compute a proper (i.e., normalized) likelihood over the input space of sequences.StatisticalModels can be further differentiated into TrainableStatisticalModels,
which can be learned from a single input DataSet, and DifferentiableStatisticalModels,
which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
These can be found in the sub-packages de.jstacs.sequenceScores.statisticalModels.trainable and de.jstacs.sequenceScores.statisticalModels.differentiable, respectively.