Title: Probability matrix decomposition models
Authors: Maris, Eric ×
De Boeck, Paul
Van Mechelen, Iven #
Issue Date: Mar-1996
Publisher: Psychometric soc
Series Title: Psychometrika vol:61 issue:1 pages:7-29
Abstract: In this paper, we consider a class of models for two-way matrices with binary entries of 0 and 1. First, we consider Boolean matrix decomposition, conceptualize it as a latent response model (LRM) and, by making use of this conceptualization, generalize it to a larger class of matrix decomposition models. Second, probability matrix decomposition (PMD) models are introduced as a probabilistic version of this larger class of deterministic matrix decomposition models. Third, an algorithm for the computation of the maximum likelihood (ML) and the maximum a posteriori (MAP) estimates of the parameters of PMD models is presented. This algorithm is an EM-algorithm, and is a special case of a more general algorithm that can be used for the whole class of LRMs. And fourth, as an example, a PMD model is applied to data on decision making in psychiatric diagnosis.
ISSN: 0033-3123
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Quantitative Psychology and Individual Differences
Onderzoeksgroep hogere cognitie en individuele verschillen (-)
× corresponding author
# (joint) last author

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