Algorithmic Probability: Fundamentals and Applications
ΠΡΠΈΡΠΊΠΎ Π·Π° ΡΠ°Π·ΠΈ Π΅Π»Π΅ΠΊΡΡΠΎΠ½Π½Π° ΠΊΠ½ΠΈΠ³Π°
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.
