A PRELIMINARY REPORT ON A GENERAL THEORY OF INDUCTIVE INFERENCE

By Ray J. Solomonoff. 2001

216 citations recorded by the source corpus.

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Citations in the corpus

Causal Inference Using the Algorithmic Markov Condition (2008, methodology): “Recall that the Kolmogorov complexity K(s) of a string s ∈ {0, 1}∗ is defined as the length of the shortest program that generates s using a previously defined universal Turing machine [13, 14, 15, 16, 17, 18, 19].”
Distinguishing Cause and Effect via Second Order Exponential Models (2009, methodology): “Here, the description length is measured in terms of algorithmic information [22, 23, 24], sometimes also called “Kolmogorov complexity”.”
Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory (2009, background): “Let s, t be two binary strings that describe the observed objects and let K ( s ) denote the algorithmic information (or “Kolmogorov complexity”), i.e., the length of the shortest program that generates s on a universal Turing machine [7, 15, 2, 1].”
The Nature of Statistical Learning Theory (2000, background): “The Idea of Algorithmic Complexity Finally, in the 1960s one of the greatest ideas of statistics and information theory was suggested: the idea of algorithmic complexity (Solomonoff, 1960), (Kolmogorov, 1965), and (Chaitin, 1966).”
Formalizing Two Problems of Realistic World-Models (2015, methodology): “That is, fix some countable set O of observations which can be sent from environment to agent, and some countable set A of actions which can be sent from agent to environment, and then consider Turing machines which take a finite list of actions and computes a new observation O.”
Prediction, learning, and games (2006, methodology): “This research eventually led him to the introduction of a universal prior probability [273–275], to be used as a prior in bayesian inference.”
The missing link between the theory and empirics of path dependence: Conceptual clarification, testability issue, and methodological implications (2010, methodology): “We recur to an intuitive approach of randomness derived from the works of mathematicians (Chaitin, 1975; Li and Vitanyi, 1997; Solomonoff, 1960), according to which a series of events appears more random than others when more information is necessary to generate it (Chaitin, 1975).”