Editing Kolmogorov complexity (section)

==Minimum message length==
{{Main|Minimum message length}}
The minimum message length principle of statistical and inductive inference and machine learning was developed by [[Chris Wallace (computer scientist)|C.S. Wallace]] and D.M. Boulton in 1968. MML is [[Bayesian probability|Bayesian]] (i.e. it incorporates prior beliefs) and information-theoretic. It has the desirable properties of statistical invariance (i.e. the inference transforms with a re-parametrisation, such as from polar coordinates to Cartesian coordinates), statistical consistency (i.e. even for very hard problems, MML will converge to any underlying model) and efficiency (i.e. the MML model will converge to any true underlying model about as quickly as is possible). C.S. Wallace and D.L. Dowe (1999) showed a formal connection between MML and algorithmic information theory (or Kolmogorov complexity).<ref>{{cite journal |citeseerx=10.1.1.17.321 |title=Minimum Message Length and Kolmogorov Complexity |journal=Computer Journal |volume=42 |issue=4 |pages=270–283 |year=1999 |last1=Wallace |first1=C. S. |last2=Dowe |first2=D. L. |doi=10.1093/comjnl/42.4.270 }}</ref>