1. A method for training a topic model, comprising: for a document within a document corpus: receiving a document representation of the document and features of the document, the document representation comprising a frequency of word occurrences within the document; processing the document representation and the features using a topic model, the processing comprising: specifying a feature/topic parameter for a feature of the document, the feature/topic parameter specifying a probability of the feature being indicative of a first topic, the feature/topic parameter based upon a first uncertainty measure that is associated with a first determination of a first deviation of the feature/topic parameter from a current feature/topic parameter for the topic model; updating the first uncertainty measure based upon a first difference measure between the feature/topic parameter and one or more previously specified feature/topic parameters for the topic model, the first difference measure representing a first range of deviation for the first uncertainty measure; specifying a document/word/topic parameter for a word within the document, the document/word/topic parameter specifying a probability of the word being indicative of a second topic, the document/word/topic parameter based upon a second uncertainty measure that is associated with a second determination of a second deviation of the document/word/topic parameter from a current document/word/topic parameter for the topic model; and updating the second uncertainty measure based upon a second difference measure between the document/word/topic parameter and one or more previously specified document/word/topic parameters for the topic model, the second difference measure representing a second range of deviation for the second uncertainty measure; and training the topic model based upon the feature/topic parameter and the document/word/topic parameter.