@article{themenheft2007webmining,
  author = {Hotho, Andreas and Stumme, Gerd},
  interhash = {39f94bf3a1663d9cec6a6cb8354a9bd9},
  intrahash = {e9535ec82afa53f44a1b37704aa9a71f},
  journal = {Künstliche Intelligenz},
  number = 3,
  pages = {5-8},
  title = {Mining the World Wide Web -- Methods, Ap-
plications, and Perspectives},
  url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758},
  year = 2007
}

@proceedings{themenheft2007webmining,
  editor = {Hotho, Andreas and Stumme, Gerd},
  interhash = {83c28b86f2ac897e906660e54e6fffc0},
  intrahash = {c73311bb72ad480d74125dbc9d94c450},
  journal = {Künstliche Intelligenz},
  number = 3,
  pages = {5-8},
  title = {Themenheft Web Mining, Künstliche Intelligenz},
  url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758},
  year = 2007
}

@book{cowell1998advanced,
  author = {Cowell, R.},
  interhash = {fe438e1412e694bba0969bc7f99310a6},
  intrahash = {aa27d5a4998c8c6967049cb99c5bd40e},
  publisher = {Learning in Graphical Models. MIT Press},
  title = {{Advanced inference in Bayesian networks}},
  url = {http://scholar.google.de/scholar.bib?q=info:PZ3Aqxv-3FgJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=0},
  year = 1998
}

@article{casella1992,
  abstract = {Computer-intensive algorithms, such as the Gibbs sampler, have become increasingly popular statistical tools, both in applied and theoretical work. The properties of such algorithms, however, may sometimes not be obvious. Here we give a simple explanation of how and why the Gibbs sampler works. We analytically establish its properties in a simple case and provide insight for more complicated cases. There are also a number of examples.},
  author = {Casella, George and George, Edward I.},
  citeulike-article-id = {1270229},
  citeulike-linkout-0 = {http://dx.doi.org/10.2307/2685208},
  citeulike-linkout-1 = {http://www.jstor.org/stable/2685208},
  doi = {10.2307/2685208},
  interhash = {ba4f08a9e4e1add859c3b2c9661728fa},
  intrahash = {d9ef3231e2903c2f5bc2ef565f87f882},
  issn = {00031305},
  journal = {The American Statistician},
  number = 3,
  pages = {167--174},
  posted-at = {2009-09-24 05:52:36},
  priority = {2},
  publisher = {American Statistical Association},
  title = {Explaining the Gibbs Sampler},
  url = {http://dx.doi.org/10.2307/2685208},
  volume = 46,
  year = 1992
}

@article{Buntine94operationsfor,
  abstract = {This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...},
  author = {Buntine, Wray L.},
  interhash = {c7dd650780467c934551356630a7b739},
  intrahash = {8952cf0d215116e038971f7c30d6d19d},
  journal = {Journal of Artificial Intelligence Research},
  pages = {159--225},
  title = {Operations for Learning with Graphical Models},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.52.696},
  volume = 2,
  year = 1994
}

@article{themenheft2007webmining,
  author = {Hotho, Andreas and Stumme, Gerd},
  interhash = {39f94bf3a1663d9cec6a6cb8354a9bd9},
  intrahash = {e9535ec82afa53f44a1b37704aa9a71f},
  journal = {Künstliche Intelligenz},
  number = 3,
  pages = {5-8},
  title = {Mining the World Wide Web -- Methods, Ap-
plications, and Perspectives},
  url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758},
  year = 2007
}

@proceedings{themenheft2007webmining,
  editor = {Hotho, Andreas and Stumme, Gerd},
  interhash = {83c28b86f2ac897e906660e54e6fffc0},
  intrahash = {c73311bb72ad480d74125dbc9d94c450},
  journal = {Künstliche Intelligenz},
  number = 3,
  pages = {5-8},
  title = {Themenheft Web Mining, Künstliche Intelligenz},
  url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758},
  year = 2007
}

@article{hotho2007mining,
  author = {Hotho, Andreas and Stumme, Gerd},
  interhash = {26915a205b66368931821165ecaf972c},
  intrahash = {92d3a5fdd786086fa12787e3e350b6af},
  journal = {Künstliche Intelligenz},
  number = 3,
  pages = {5-8},
  title = {Mining the World Wide Web},
  url = {http://kobra.bibliothek.uni-kassel.de/bitstream/urn:nbn:de:hebis:34-2008021320337/3/HothoStummeMiningWWW.pdf},
  vgwort = {20},
  year = 2007
}

@book{mitchell97,
  author = {Mitchell, Tom M.},
  interhash = {479a66c32badb3a455fbdcf8e6633a5d},
  intrahash = {3e79734ee1a6e49aee02ffd108224d1c},
  publisher = {McGraw-Hill},
  title = {Machine Learning},
  year = 1997
}