This article is dedicated to Ray Solomonoff (1926–2009), the discoverer and inventor of Universal Induction. |

## 1. Introduction

#### 1.1. Overview of Article

## 2. Broader Context

#### 2.1. Induction versus Deduction

#### 2.2. Prediction versus Induction

#### 2.3. Prediction, Concept Learning, Classification, Regression

#### 2.4. Prediction with Expert Advice versus Bayesian Learning

#### 2.5. No Free Lunch versus Occam’s Razor

#### 2.6. Non-Monotonic Reasoning

#### 2.7. Solomonoff Induction

## 3. Probability

#### 3.1. Frequentist

#### 3.2. Objectivist

#### Kolmogorov’s Probability Axioms.

- If A and B are events, then the intersection $A\cap B$, the union $A\cup B$, and the difference $A\backslash B$ are also events.
- The sample space Ω and the empty set $\left\{\right\}$ are events.
- There is a function P that assigns non-negative real numbers, called probabilities, to each event.
- $P(\Omega )=1$ and $P\left(\right\{\left\}\right)=0$.
- $P(A\cup B)=P\left(A\right)+P\left(B\right)-P(A\cap B)$
- For a decreasing sequence ${A}_{1}\supset {A}_{2}\supset {A}_{3}...$of events with ${\cap}_{n}{A}_{n}=\left\{\right\}$ we have ${lim}_{n\to \infty}P\left({A}_{n}\right)=\phantom{\rule{3.33333pt}{0ex}}0$

#### 3.3. Subjectivist

#### Cox’s Axioms for Beliefs.

- The degree of belief in an event B, given that event A has occurred can be characterized by a real-valued function $Bel\left(B\right|A)$.
- $Bel(\Omega \backslash B|A)$ is a twice differentiable function of $Bel\left(B\right|A)$ for $A\ne \left\{\right\}$.
- $Bel(B\cap C|A)$ is a twice differentiable function of $Bel\left(C\right|B\cap A)$ and $Bel\left(B\right|A)$ for $B\cap A\ne \left\{\right\}$.

## 4. Bayesianism for Prediction

#### 4.1. Notation

#### 4.2. Thomas Bayes

#### 4.3. Models, Hypotheses and Environments

#### 4.4. Bayes Theorem

#### 4.5. Partial Hypotheses

#### 4.6. Sequence Prediction

#### 4.7. Bayes Mixture

#### 4.8. Expectation

#### 4.9. Convergence Results

#### 4.10. Bayesian Decisions

#### 4.11. Continuous Environment Classes

#### 4.12. Choosing the Model Class

## 5. History

#### 5.1. Epicurus

#### 5.2. Sextus Empiricus and David Hume

#### 5.3. William of Ockham

#### 5.4. Pierre-Simon Laplace and the Rule of Succession

#### 5.5. Confirmation Problem

#### 5.6. Patrick Maher Does not Capture the Logic of Confirmation

#### 5.7. Black Ravens Paradox

**$B\left(x\right)$**are true confirms the hypothesis “all x which are A are also B” or $\forall xA\left(x\right)\Rightarrow B\left(x\right)$. This is known as Nicods condition which has been seen as a highly intuitive property but it is not universally accepted [9]. However even if there are particular situations where it does not hold it is certainly true in the majority of situations and in these situations the following problem remains.

#### 5.8. Alan Turing

#### 5.9. Andrey Kolmogorov

## 6. How to Choose the Prior

#### 6.1. Subjective versus Objective Priors

#### 6.2. Indifference Principle

#### 6.3. Reparametrization Invariance

#### 6.4. Regrouping Invariance

#### 6.5. Universal Prior

## 7. Solomonoff Universal Prediction

#### 7.1. Universal Bayes Mixture

#### 7.2. Deterministic Representation

#### 7.3. Old Evidence and New Hypotheses

#### 7.4. Black Ravens Paradox Using Solomonoff

## 8. Prediction Bounds

#### 8.1. Total Bounds

#### 8.2. Instantaneous Bounds

#### 8.3. Future Bounds

#### 8.4. Universal is Better than Continuous $\mathcal{M}$

## 9. Approximations and Applications

#### 9.1. Golden Standard

“in spite of its incomputability, Algorithmic Probability can serve as a kind of `Gold standard’ for inductive systems”—Ray Solomonoff, 1997

#### 9.2. Minimum Description Length Principle

#### 9.3. Resource Bounded Complexity and Prior

#### 9.4. Context Tree Weighting

#### 9.5. Universal Similarity Measure

#### 9.6. Universal Artificial Intelligence

## 10. Discussion

#### 10.1. Prior Knowledge

#### 10.2. Dependence on Universal Turing Machine U

#### 10.3. Advantages and Disadvantages

- General total bounds for generic class, prior and loss function as well as instantaneous and future bounds for both the i.i.d. and universal cases.
- The bound for continuous classes and the more general result that M works well even in non-computable environments.
- Solomonoff satisfies both reparametrization and regrouping invariance.
- Solomonoff solves many persistent philosophical problems such as the zero prior and confirmation problem for universal hypotheses. It also deals with the problem of old evidence and we argue that it should solve the black ravens paradox.
- The issue of incorporating prior knowledge is also elegantly dealt with by providing two methods which theoretically allow any knowledge with any degree of relevance to be most effectively exploited.

#### 10.4. Conclusions

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**Figure 1.**Schematic graph of prefix Kolmogorov complexity $K\left(x\right)$ with string x interpreted as integer.

Induction | ⇔ | Deduction | |
---|---|---|---|

Type of inference: | generalization/prediction | ⇔ | specialization/derivation |

Framework: | probability axioms | $\widehat{=}$ | logical axioms |

Assumptions: | prior | $\widehat{=}$ | non-logical axioms |

Inference rule: | Bayes rule | $\widehat{=}$ | modus ponens |

Results: | posterior | $\widehat{=}$ | theorems |

Universal scheme: | Solomonoff probability | $\widehat{=}$ | Zermelo-Fraenkel set theory |

Universal inference: | universal induction | $\widehat{=}$ | universal theorem prover |

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