Safe-Infered Safe-Infered Safe-Infered:Custom random variable representing the LDA Gibbs sampler BAbstract type holding the LDA model, and the inverse count tables  LDA model Inverse document-topic counts Inverse word-topic counts @Abstract type holding the settings and the state of the sampler Document-topic counts Word-topic counts  Topic counts 1alpha * K Dirichlet parameter (topic sparseness) +beta Dirichlet parameter (word sparseness) Number of topics K Number of unique words  initial k a b initializes model with k topics, a/k alpha  hyperparameter and b beta hyperparameter.  finalize m* creates a finalized model from LDA model m  pass batch1 runs one pass of Gibbs sampling on documents in batch runSampler seed m s runs sampler s with seed and initial  model m&. The random number generator used is  System.Random.Mersenne.Pure64. runLDA seed n m ds& creates and runs an LDA sampler with seed  for n passes with initial model m on the batch of documents  ds&. The random number generator used is  System.Random.Mersenne.Pure64. Remove zero counts from the doc/ topic table docTopicWeights m doc* returns unnormalized topic probabilities " for document doc given LDA model m   $          !" lda-0.0.1NLP.LDANLP.LDA.UnboxedMaybeVector NLP.LDA.UtilsSampler Finalizedmodel topicDocs topicWordsLDA docTopics wordTopicstopicsalphasumbetatopicNumvSizeTable1DTable2DDocWZDinitialfinalizepass runSamplerrunLDAcompressdocTopicWeights$fVectorVectorMaybe$fMVectorMVectorMaybe $fUnboxMaybecount