# Difference between revisions of "Solomonoff induction"

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− | Solomonoff | + | Ray Solomonoff defined an inference system will learn to correctly predict any computable sequence with only the absolute minimum amount of data. It would thus, in some sense, be the perfect universal prediction algorithm, if only it were computable. To summarize it very informally, '''Solomonoff induction''' works by starting with all possible hypotheses as represented by computer programs on some universal Turing machine, weighted by their simplicity (2^-''n'', where ''n'' is the program length), then discarding those hypotheses that are inconsistent with the data. By weighting by simplicity, the system automatically incorporates a form of Occam's razor, which is why it has been playfully referred to as ''Solomonoff's lightsaber''. |

==See also== | ==See also== | ||

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*[http://www.scholarpedia.org/article/Algorithmic_probability Algorithmic probability] on Scholarpedia | *[http://www.scholarpedia.org/article/Algorithmic_probability Algorithmic probability] on Scholarpedia | ||

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[[Category:Math]] | [[Category:Math]] | ||

[[Category:Decision theory]] | [[Category:Decision theory]] |

## Revision as of 09:00, 19 November 2009

Ray Solomonoff defined an inference system will learn to correctly predict any computable sequence with only the absolute minimum amount of data. It would thus, in some sense, be the perfect universal prediction algorithm, if only it were computable. To summarize it very informally, **Solomonoff induction** works by starting with all possible hypotheses as represented by computer programs on some universal Turing machine, weighted by their simplicity (2^-*n*, where *n* is the program length), then discarding those hypotheses that are inconsistent with the data. By weighting by simplicity, the system automatically incorporates a form of Occam's razor, which is why it has been playfully referred to as *Solomonoff's lightsaber*.

## See also

## References

- Algorithmic probability on Scholarpedia