Another kind of anomalous records ist presented here at videolectures.net. The problem of detecting anomalous records in categorical datasets is solved by creating a model of normal data, later comparing test records against it. A probabilistic approach builds a likelihood model from the training data. Based on the complete record likelihood the records are tested for anomalousness. It is shown that there is a better way of detecting anomalies than with classical bays net approaches.