Probabilistic Reasoning as Information Compression by Multiple
  Alignment, Unification and Search: An Introduction and Overview
  This article introduces the idea that probabilistic reasoning (PR) may be
understood as "information compression by multiple alignment, unification and
search" (ICMAUS). In this context, multiple alignment has a meaning which is
similar to but distinct from its meaning in bio-informatics, while unification
means a simple merging of matching patterns, a meaning which is related to but
simpler than the meaning of that term in logic.
  A software model, SP61, has been developed for the discovery and formation of
'good' multiple alignments, evaluated in terms of information compression. The
model is described in outline.
  Using examples from the SP61 model, this article describes in outline how the
ICMAUS framework can model various kinds of PR including: PR in best-match
pattern recognition and information retrieval; one-step 'deductive' and
'abductive' PR; inheritance of attributes in a class hierarchy; chains of
reasoning (probabilistic decision networks and decision trees, and PR with
'rules'); geometric analogy problems; nonmonotonic reasoning and reasoning with
default values; modelling the function of a Bayesian network.
