## Algorithmic Probability

In algorithmic information theory, **algorithmic (Solomonoff) probability** is a method of assigning a probability to each hypothesis (algorithm/program) that explains a given observation, with the simplest hypothesis (the shortest program) having the highest probability and the increasingly complex hypotheses (longer programs) receiving increasingly small probabilities. These probabilities form *a priori* a probability distribution for the observation, which Ray Solomonoff proved to be machine-invariant (called the invariance theorem) and can be used with Bayes' theorem to predict the most likely continuation of that observation. A theoretic computer, the universal Turing machine, is used for the computer operations.

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### Some articles on algorithmic probability:

**Algorithmic Probability**

... In

**algorithmic**information theory,

**algorithmic**(Solomonoff)

**probability**is a method of assigning a

**probability**to each hypothesis (algorithm/program) that ... These probabilities form a priori a

**probability**distribution for the observation, which Ray Solomonoff proved to be machine-invariant (called the invariance theorem ... Solomonoff invented the concept of

**algorithmic probability**with its associated invariance theorem around 1960 ...

... Originally

**algorithmic**induction methods extrapolated ordered sequences of strings ... and began research on new applications of

**Algorithmic Probability**...

**Algorithmic Probability**and Solomonoff Induction have many advantages for Artificial Intelligence ...

### Famous quotes containing the word probability:

“Liberty is a blessing so inestimable, that, wherever there appears any *probability* of recovering it, a nation may willingly run many hazards, and ought not even to repine at the greatest effusion of blood or dissipation of treasure.”

—David Hume (1711–1776)