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Reconstructability Analysis, Information Theory, Probabilistic graphical modeling, Multivariate analysis discrete multivariate modeling, Data mining


This paper is an overview of reconstructability analysis (RA), a discrete multivariate modeling methodology developed in the systems literature; an earlier version of this tutorial is Zwick (2001). RA was derived from Ashby (1964), and was developed by Broekstra, Cavallo, Cellier Conant, Jones, Klir, Krippendorff, and others (Klir, 1986, 1996). RA resembles and partially overlaps log‐line (LL) statistical methods used in the social sciences (Bishop et al., 1978; Knoke and Burke, 1980). RA also resembles and overlaps methods used in logic design and machine learning (LDL) in electrical and computer engineering (e.g. Perkowski et al., 1997). Applications of RA, like those of LL and LDL modeling, are diverse, including time‐series analysis, classification, decomposition, compression, pattern recognition, prediction, control, and decision analysis.

RA involves the set‐theoretic modeling of relations and mappings and the information‐theoretic modeling of probability/frequency distributions. Its different uses can be categorized using the dimensions of variable, system, data, problem, and method‐types shown in Table I. These will now be briefly discussed. Section 2 explains RA in more detail. Section 3 gives examples, Section 4 discusses software, and Section 5 offers a concluding discussion.


Author's version of an article which subsequently appeared in Kybernetes, published by Emerald Group Publishing Limited. The version of record may be found at



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