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The most spectacular use of the EM algorithm is for automatic (unsupervised) classification in the AUTOCLASS model (see next subsection). Segmentation - Latent Variables Segmentation and latent variable analysis aims at describing the data set as a collection of subsets, each having simpler descriptions than the full data matrix. The related technique of cluster analysis, although not described here, can also be given a Bayesian interpretation as an effort to describe a data set as a collection of clusters with small variation around the center of each cluster.

But if data is missing because of unavailability of equipment, it is probably not - unless maybe if the investigation is related to hospital quality. Assuming that data is missing completely at random, it is relatively easy to get an adequate analysis. It is not necessary to waste entire cases just because they have a missing item. Most of the analyses made refer only to a small number of columns, and these columns can be compared for all cases that have no missing data in these particular columns.

2003). Probability theory: The logic of science. Cambridge University Press, ISBN: 0521592712. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. , & Tomkins, A. (2001). Recommendation systems: A probabilistic analysis. JCSS: Journal of Computer and System Sciences 63(1): 42–61. Lauritzen, S. L. (1996), Graphical models. Oxford, UK: Clarendon Press. Madigan, D. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window.

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