Three Kinds of Probabilistic Induction: Universal Distributions and Convergence Theorems

Author: Ray Solomonoff
Links: PDF

See also: Complexity-Based Induction Systems

Summary

Foundational work on algorithmic probability and universal induction — the theoretical basis for optimal prediction and learning from a computability-theoretic perspective.

Relevance to Hyperon

Solomonoff induction provides the theoretical gold standard for learning that informs Hyperon's approach to pattern recognition and probabilistic reasoning.