Minimum message length encoding is a technique of inductive inference with theoretical and practical advantages. It allows the posterior odds-ratio of two theories or hypotheses to be calculated. Here it is applied to problems of aligning or relating two strings, in particular two biological macromolecules. We compare the r-theory, that the strings are related, with the null-theory, that they are not related. If they are related, the probabilities of the various alignments can be calculated. This is done for one-, three-, and five-state models of relation or mutation. These correspond to linear and piecewise linear cost functions on runs of insertions and deletions. We describe how to estimate parameters of a model. The validity of a model is itself an hypothesis and can be objectively tested. This is done on real DNA strings and on artificial data. The tests on artificial data indicate limits on what can be inferred in various situations. The tests on real DNA support either the three- or five-state models over the one-state model. Finally, a fast, approximate minimum message length string comparison algorithm is described.

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A comparison of inductive inference known as minimum message length encoding is applied to string comparison in molecular biology. The question of whether or not two strings are related and, if so, of how they are related and the problem of finding a good theory of string mutation are treated as inductive inference problems. The method allows the posterior odds-ratio of two string alignments or of two models of string mutation to be computed. The connection between models of mutation and existing string alignment algorithms is made explicit. A fast minimum message length alignment algorithm is also described.