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Solomonoff's theory of inductive inference

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Solomonoff's theory of inductive inference is a mathematical theory of induction introduced by Ray Solomonoff, based on probability theory and theoretical computer science.[1][2][3] 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.

Solomonoff proved that this induction is incomputable, but noted that "this incomputability is of a very benign kind", and that it "in no way inhibits its use for practical prediction".[2]

Solomonoff's induction naturally formalizes Occam's razor[4][5][6][7][8] by assigning larger prior credences to theories that require a shorter algorithmic description.



The theory is based in philosophical foundations, and was founded by Ray Solomonoff around 1960.[9] It is a mathematically formalized combination of Occam's razor[4][5][6][7][8] and the Principle of Multiple Explanations.[10] All computable theories which perfectly describe previous observations are used to calculate the probability of the next observation, with more weight put on the shorter computable theories. Marcus Hutter's universal artificial intelligence builds upon this to calculate the expected value of an action.


Solomonoff's induction has been argued to be the computational formalization of pure Bayesianism.[3] To understand, recall that Bayesianism derives the posterior probability of a theory given data by applying Bayes rule, which yields

where theories are alternatives to theory . For this equation to make sense, the quantities and must be well-defined for all theories and . In other words, any theory must define a probability distribution over observable data . Solomonoff's induction essentially boils down to demanding that all such probability distributions be computable.

Interestingly, the set of computable probability distributions is a subset of the set of all programs, which is countable. Similarly, the sets of observable data considered by Solomonoff were finite. Without loss of generality, we can thus consider that any observable data is a finite bit string. As a result, Solomonoff's induction can be defined by only invoking discrete probability distributions.

Solomonoff's induction then allows to make probabilistic predictions of future data , by simply obeying the laws of probability. Namely, we have . This quantity can be interpreted as the average predictions of all theories given past data , weighted by their posterior credences .


The proof of the "razor" is based on the known mathematical properties of a probability distribution over a countable set. These properties are relevant because the infinite set of all programs is a denumerable set. The sum S of the probabilities of all programs must be exactly equal to one (as per the definition of probability) thus the probabilities must roughly decrease as we enumerate the infinite set of all programs, otherwise S will be strictly greater than one. To be more precise, for every > 0, there is some length l such that the probability of all programs longer than l is at most . This does not, however, preclude very long programs from having very high probability.

Fundamental ingredients of the theory are the concepts of algorithmic probability and Kolmogorov complexity. The universal prior probability of any prefix p of a computable sequence x is the sum of the probabilities of all programs (for a universal computer) that compute something starting with p. Given some p and any computable but unknown probability distribution from which x is sampled, the universal prior and Bayes' theorem can be used to predict the yet unseen parts of x in optimal fashion.

Mathematical guarantees[edit]

Solomonoff's completeness[edit]

The remarkable property of Solomonoff's induction is its completeness. In essence, the completeness theorem guarantees that the expected cumulative errors made by the predictions based on Solomonoff's induction are upper-bounded by the Kolmogorov complexity of the (stochastic) data generating process. The errors can be measured using the Kullback–Leibler divergence or the square of the difference between the induction's prediction and the probability assigned by the (stochastic) data generating process.

Solomonoff's uncomputability[edit]

Unfortunately, Solomonoff also proved that Solomonoff's induction is uncomputable. In fact, he showed that computability and completeness are mutually exclusive: any complete theory must be uncomputable. The proof of this is derived from a game between the induction and the environment. Essentially, any computable induction can be tricked by a computable environment, by choosing the computable environment that negates the computable induction's prediction. This fact can be regarded as an instance of the no free lunch theorem.

Modern applications[edit]

Artificial intelligence[edit]

Though Solomonoff's inductive inference is not computable, several AIXI-derived algorithms approximate it in order to make it run on a modern computer. The more computing power they are given, the closer their predictions are to the predictions of inductive inference (their mathematical limit is Solomonoff's inductive inference).[11][12][13]

Another direction of inductive inference is based on E. Mark Gold's model of learning in the limit from 1967 and has developed since then more and more models of learning.[14] The general scenario is the following: Given a class S of computable functions, is there a learner (that is, recursive functional) which for any input of the form (f(0),f(1),...,f(n)) outputs a hypothesis (an index e with respect to a previously agreed on acceptable numbering of all computable functions; the indexed function may be required consistent with the given values of f). A learner M learns a function f if almost all its hypotheses are the same index e, which generates the function f; M learns S if M learns every f in S. Basic results are that all recursively enumerable classes of functions are learnable while the class REC of all computable functions is not learnable.[citation needed] Many related models have been considered and also the learning of classes of recursively enumerable sets from positive data is a topic studied from Gold's pioneering paper in 1967 onwards. A far reaching extension of the Gold’s approach is developed by Schmidhuber's theory of generalized Kolmogorov complexities,[15] which are kinds of super-recursive algorithms.

See also[edit]


  1. ^ Zenil, Hector (2011-02-11). Randomness Through Computation: Some Answers, More Questions. World Scientific. ISBN 978-981-4462-63-1.
  2. ^ a b Solomonoff, Ray J. (2009), Emmert-Streib, Frank; Dehmer, Matthias (eds.), "Algorithmic Probability: Theory and Applications", Information Theory and Statistical Learning, Boston, MA: Springer US, pp. 1–23, doi:10.1007/978-0-387-84816-7_1, ISBN 978-0-387-84816-7, retrieved 2020-07-21
  3. ^ a b Hoang, Lê Nguyên (2020). The equation of knowledge : from Bayes' rule to a unified philosophy of science (First ed.). Boca Raton, FL. ISBN 978-0-367-85530-7. OCLC 1162366056.{{cite book}}: CS1 maint: location missing publisher (link)
  4. ^ a b JJ McCall. Induction: From Kolmogorov and Solomonoff to De Finetti and Back to Kolmogorov – Metroeconomica, 2004 – Wiley Online Library.
  5. ^ a b D Stork. Foundations of Occam's razor and parsimony in learning from ricoh.com – NIPS 2001 Workshop, 2001
  6. ^ a b A.N. Soklakov. Occam's razor as a formal basis for a physical theory from arxiv.org – Foundations of Physics Letters, 2002 – Springer
  7. ^ a b Jose Hernandez-Orallo (1999). "Beyond the Turing Test" (PDF). Journal of Logic, Language and Information. 9.
  8. ^ a b M Hutter. On the existence and convergence of computable universal priors arxiv.org – Algorithmic Learning Theory, 2003 – Springer
  9. ^ Samuel Rathmanner and Marcus Hutter. A philosophical treatise of universal induction. Entropy, 13(6):1076–1136, 2011
  10. ^ Ming Li and Paul Vitanyi, An Introduction to Kolmogorov Complexity and Its Applications. Springer-Verlag, N.Y., 2008p 339 ff.
  11. ^ J. Veness, K.S. Ng, M. Hutter, W. Uther, D. Silver. "A Monte Carlo AIXI Approximation" – Arxiv preprint, 2009 arxiv.org
  12. ^ J. Veness, K.S. Ng, M. Hutter, D. Silver. "Reinforcement Learning via AIXI Approximation" Arxiv preprint, 2010 – aaai.org
  13. ^ S. Pankov. A computational approximation to the AIXI model from agiri.org – Artificial general intelligence, 2008: proceedings of …, 2008 – books.google.com
  14. ^ Gold, E. Mark (1967). "Language identification in the limit" (PDF). Information and Control. 10 (5): 447–474. doi:10.1016/S0019-9958(67)91165-5.
  15. ^ J. Schmidhuber (2002). "Hierarchies of generalized Kolmogorov complexities and nonenumerable universal measures computable in the limit" (PDF). International Journal of Foundations of Computer Science. 13 (4): 587–612. doi:10.1142/S0129054102001291.


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External links[edit]