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Solomonoff's theory of inductive inference

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Solomonoff's theory of inductive inference proves that, under its common sense assumptions (axioms), the best possible scientific model is the shortest algorithm that generates the empirical data under consideration. In addition to the choice of data, other assumptions are that, to avoid the post-hoc fallacy, the programming language must be chosen prior to the data and that the environment being observed is generated by an unknown algorithm. This is also called a theory of induction. Due to its basis in the dynamical (state-space model) character of Algorithmic Information Theory, it encompasses statistical as well as dynamical information criteria for model selection. It 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.

Solomonoff's theory of inductive inference - Wikipedia Jump to content From Wikipedia, the free encyclopedia Mathematical theory This article has multiple issues. Please help improve it or discuss these issues on the talk page . ( Learn how and when to remove these messages ) Some of this article's listed sources may not be reliable . Please help improve this article by looking for better, more reliable sources. Unreliable citations may be challenged and removed. ( February 2024 ) ( Learn how and when to remove this message ) This article may be confusing or unclear to readers . In particular,

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