Algorithmic Probability: Fundamentals and Applications

In the field of algorithmic information theory, algorithmic probability is a mathematical method that assigns a prior probability to a given observation. This method is sometimes referred to as Solomonoff probability. In the 1960s, Ray Solomonoff was the one who came up with the idea. It has applications in the theory of inductive reasoning as well as the analysis of algorithms. Solomonoff combines Bayes' rule and the technique in order to derive probabilities of prediction for an algorithm's future outputs. He does this within the context of his broad theory of inductive inference.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Algorithmic Probability

Chapter 2: Kolmogorov Complexity

Chapter 3: Gregory Chaitin

Chapter 4: Ray Solomonoff

Chapter 5: Solomonoff's Theory of Inductive Inference

Chapter 6: Algorithmic Information Theory

Chapter 7: Algorithmically Random Sequence

Chapter 8: Minimum Description Length

Chapter 9: Computational Learning Theory

Chapter 10: Inductive Probability

(II) Answering the public top questions about algorithmic probability.

(III) Real world examples for the usage of algorithmic probability in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of algorithmic probability' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of algorithmic probability.