Solomonoff's theory of inductive inference
Solomonoff's theory of inductive inference is a mathematical proof that if a universe is generated by an algorithm, then observations of that universe, encoded as a dataset, are best predicted by the smallest executable archive of that dataset. This formalization of Occam's razor for induction was introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory.
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- enSolomonoff's theory of inductive inference is a mathematical proof that if a universe is generated by an algorithm, then observations of that universe, encoded as a dataset, are best predicted by the smallest executable archive of that dataset. This formalization of Occam's razor for induction was introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory.
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- enSolomonoff's theory of inductive inference is a mathematical proof that if a universe is generated by an algorithm, then observations of that universe, encoded as a dataset, are best predicted by the smallest executable archive of that dataset. This formalization of Occam's razor for induction was introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory.
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- www.cs.auckland.ac.nz/CDMTCS/researchreports/300nick.pdf
- dl.acm.org/doi/pdf/10.1145/356914.356918%7C
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- AIXI
- Algorithm
- Algorithmic information theory
- Algorithmic probability
- Bayes' theorem
- Bayesian inference
- Bayesian probability
- Bit array
- Carl Herbert Smith
- Category:Algorithmic information theory
- Category:Bayesian statistics
- Category:Inductive reasoning
- Category:Machine learning
- Category:Statistical inference
- Computability
- Computable
- Computer science
- Countable set
- E. Mark Gold
- Event loop
- Expected value
- Inductive inference
- Inductive probability
- Inductive reasoning
- Kolmogorov complexity
- Kullback–Leibler divergence
- Language identification in the limit
- Limit (math)
- Martin Davis (mathematician)
- Mill's methods
- Minimum description length
- Minimum message length
- MIT Press
- New riddle of induction
- No free lunch theorem
- Occam's razor
- Posterior probability
- Principle of Multiple Explanations
- Prior probability
- Probability
- Probability theory
- Problem of induction
- Ray Solomonoff
- Stephen Kleene
- Super-recursive algorithm
- Turing machine
- Universal artificial intelligence
- Universal computer
- Wikipedia:JARGON
- William Gasarch
- SameAs
- m.024cv2
- Q14947941
- Teoria da Inferência Indutiva de Solomonoff
- Vj2i
- نظرية سولومونوف في الاستدلال الاستقرائي
- 所罗门诺夫的归纳推理理论
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- Category:Algorithmic information theory
- Category:Bayesian statistics
- Category:Inductive reasoning
- Category:Machine learning
- Category:Statistical inference
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