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  1. Article ; Online: Clustgrams

    Luosto Panu / Kontkanen Petri

    Open Computer Science, Vol 1, Iss 4, Pp 466-

    an extension to histogram densities based on the minimum description length principle

    2011  Volume 481

    Keywords density estimation ; minimum description length (mdl) principle ; clustering ; histograms ; Electronic computers. Computer science ; QA75.5-76.95
    Language English
    Publishing date 2011-12-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: NML Computation Algorithms for Tree-Structured Multinomial Bayesian Networks

    Kontkanen Petri / Wettig Hannes / Myllymäki Petri

    EURASIP Journal on Bioinformatics and Systems Biology, Vol 2007, Iss 1, p

    2007  Volume 90947

    Abstract: Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL) ... ...

    Abstract Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL) principle is a theoretically well-founded, general framework for performing statistical inference. The mathematical formalization of MDL is based on the normalized maximum likelihood (NML) distribution, which has several desirable theoretical properties. In the case of discrete data, straightforward computation of the NML distribution requires exponential time with respect to the sample size, since the definition involves a sum over all the possible data samples of a fixed size. In this paper, we first review some existing algorithms for efficient NML computation in the case of multinomial and naive Bayes model families. Then we proceed by extending these algorithms to more complex, tree-structured Bayesian networks.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2007-01-01T00:00:00Z
    Publisher Springer
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: NML computation algorithms for tree-structured multinomial Bayesian networks.

    Kontkanen, Petri / Wettig, Hannes / Myllymäki, Petri

    EURASIP journal on bioinformatics & systems biology

    2008  , Page(s) 90947

    Abstract: Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL) ... ...

    Abstract Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL) principle is a theoretically well-founded, general framework for performing statistical inference. The mathematical formalization of MDL is based on the normalized maximum likelihood (NML) distribution, which has several desirable theoretical properties. In the case of discrete data, straightforward computation of the NML distribution requires exponential time with respect to the sample size, since the definition involves a sum over all the possible data samples of a fixed size. In this paper, we first review some existing algorithms for efficient NML computation in the case of multinomial and naive Bayes model families. Then we proceed by extending these algorithms to more complex, tree-structured Bayesian networks.
    Language English
    Publishing date 2008-04-02
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2233385-X
    ISSN 1687-4153 ; 1687-4145
    ISSN (online) 1687-4153
    ISSN 1687-4145
    DOI 10.1155/2007/90947
    Database MEDical Literature Analysis and Retrieval System OnLINE

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