Universal Probability

Universal Probability is a concept in information theory and computer science that assigns a likelihood to any possible data or outcome based on the idea of the simplest explanation. It measures how probable a piece of data is across all possible algorithms or models, favoring those that are simple and compressible. In essence, it predicts the chance of observing data by considering all algorithms that could produce it, giving higher probability to those that do so with less complexity. This approach helps in understanding and modeling patterns, learning, and compression in data regardless of its specific source.