Summary
A binary matrix representing the presence of traits and objects is given. We consider the task to detect a subblock of traits and objects such that for each trait its frequency of occurrence within these objects is highly increased. A Bayesian framework is specified and the Gibbs sampler is used to approximate the posterior distribution. The necessary extensions to use this procedure for kinship analysis in prehistoric anthropology are outlined.
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© 1996 Springer-Verlag Berlin · Heidelberg
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Vach, W., Alt, K.W. (1996). Detection of Blocks in a Binary Matrix — A Bayesian Approach. In: Gaul, W., Pfeifer, D. (eds) From Data to Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79999-0_23
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DOI: https://doi.org/10.1007/978-3-642-79999-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60354-2
Online ISBN: 978-3-642-79999-0
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