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K. Sjolander, K. Karplus, M. Brown, R. Hughey, A. Krogh, I. S. Mian and D. Haussler, “Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology,” Computer Applications in the Biosciences, Vol. 12, No. 4, 1996, pp. 327-345.

has been cited by the following article:

• TITLE: Dirichlet Compound Multinomials Statistical Models

  AUTHORS: Paola Cerchiello, Paolo Giudici

  KEYWORDS: Textual Data Analysis; Mixture Models; Ontology Schema; Reputational Risk

  JOURNAL NAME: Applied Mathematics, Vol.3 No.12A, December 31, 2012

  ABSTRACT: This contribution deals with a generative approach for the analysis of textual data. Instead of creating heuristic rules forthe representation of documents and word counts, we employ a distribution able to model words along texts considering different topics. In this regard, following Minka proposal (2003), we implement a Dirichlet Compound Multinomial (DCM) distribution, then we propose an extension called sbDCM that takes explicitly into account the different latent topics that compound the document. We follow two alternative approaches: on one hand the topics can be unknown, thus to be estimated on the basis of the data, on the other hand topics are determined in advance on the basis of a predefined ontological schema. The two possible approaches are assessed on the basis of real data.