A Formal Theory of Inductive Inference, Part I

Solomonoff, R. J. (1964). A Formal Theory of Inductive Inference, Part I. Information and Control, 7(1), 1-22.

Type

Journal article

Intellectual base

Primary research

Year

1964

Volume

7

Issue

1

Pages

1-22

ISSN

0019-9958

DOI

10.1016/s0019-9958(64)90223-2

DOI registrant

Elsevier

Link

https://doi.org/10.1016/S0019-9958(64)90223-2

Cited from

information_theory, mathematics, philosophy, systems_cybernetics

Cited by

5 citations across 5 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Complexity
  • Kolmogorov complexity (independently defined by Solomonoff, Kolmogorov, Chaitin in the 1960s) measures the length of the shortest program generating a string, providing an information-theoretic foundation independent of any specific machine

    This sourceThree independent foundations of Kolmogorov complexity (shortest program generating a string; machine-independent).

• Compression
  • The construct manages the complexity of representing large information streams by providing algorithms parameterized by source model and distortion constraint, by tying representation size to the underlying entropy (a deep connection to probability theory)

    This sourceFounds algorithmic probability and universal inductive inference, tying predictive probability to shortest description. SUPPORTS marker 177 only loosely — the marker sits on 'tying representation size to the underlying entropy (a deep connection to probability theory)'; Solomonoff supplies the algorithmic-probability link, defensible but indirect for the entropy-size claim. DOI verified. See flag.

• Parsimony (Occam's Razor)
  • and Solomonoff universal induction

    This source(Originating treatment of algorithmic probability and universal inductive inference; establishes theoretical foundations for learning from data; parallel independent work to Kolmogorov and Chaitin.)

• Randomness
  • and Solomonoff (1964)

    This source(Originating treatment of algorithmic probability and universal inductive inference; establishes theoretical foundations for learning from data; parallel independent work to Kolmogorov and Chaitin.)

Domain-specific

• Solomonoff Induction
  • It combines Bayesian prediction with algorithmic probability: retain all programmatic explanations compatible with the observations, weight shorter programs more heavily, and average their implied continuations rather than selecting only one model.

Verification

Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.

Does it back the claim? Not recorded. Neither this nor any other of the 5 citations of this work carries a recorded support check.

Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://doi.org/10.1145/321356.321363 ×1

Registry ID ref:59c2ecb73aff · see in the full table