/// <summary>
/// Computes the logarithm of the model evidence corrections required in batched training.
/// </summary>
/// <returns>The logarithm of the model evidence corrections.</returns>
/// <remarks>
/// When the training data is split into several batches, it is necessary to eliminate evidence contributions
/// which would otherwise be double counted. In essence, evidence corrections remove the superfluous contributions
/// of factors which are shared across data batches, such as priors and constraints. To compute the evidence
/// contributions of the factors shared across batches, one can compute the evidence on an empty batch.
/// </remarks>
protected virtual double ComputeLogEvidenceCorrection()
{
// Correct the below empty batch correction and add the missing evidence contribution
// for the Replicate operator on the weights for all batches
double logModelEvidenceCorrection = InferenceAlgorithmUtilities.ComputeLogEvidenceCorrectionReplicateAllBatches(this.BatchWeightOutputMessages);
// Compute the evidence contribution of all factors shared across batches and remove it for all but one batch.
logModelEvidenceCorrection -= (this.BatchCount - 1) * this.ComputeLogEvidenceContributionEmptyBatch();
return(logModelEvidenceCorrection);
}
/// <summary>
/// Computes the logarithm of the model evidence corrections required in batched training.
/// </summary>
/// <returns>The logarithm of the model evidence corrections.</returns>
/// <remarks>
/// When the training data is split into several batches, it is necessary to eliminate evidence contributions
/// which would otherwise be double counted. In essence, evidence corrections remove the superfluous contributions
/// of factors which are shared across data batches, such as priors and constraints. To compute the evidence
/// contributions of the factors shared across batches, one can compute the evidence on an empty batch.
/// </remarks>
protected override double ComputeLogEvidenceCorrection()
{
// Correct the below base correction and add the missing evidence contribution
// for the Replicate operator on the weight precision rates for all batches
double logModelEvidenceCorrection =
InferenceAlgorithmUtilities.ComputeLogEvidenceCorrectionReplicateAllBatches(this.batchWeightPrecisionRateOutputMessages);
// Compute base evidence correction
logModelEvidenceCorrection += base.ComputeLogEvidenceCorrection();
return(logModelEvidenceCorrection);
}