Cargando…

Algorithmic Randomness and Complexity

Intuitively, a sequence such as 101010101010101010... does not seem random, whereas 101101011101010100..., obtained using coin tosses, does. How can we reconcile this intuition with the fact that both are statistically equally likely? What does it mean to say that an individual mathematical object s...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Downey, Rodney G. (Autor), Hirschfeldt, Denis R. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2010.
Edición:1st ed. 2010.
Colección:Theory and Applications of Computability, In cooperation with the association Computability in Europe,
Temas:
Acceso en línea:Texto Completo

MARC

LEADER 00000nam a22000005i 4500
001 978-0-387-68441-3
003 DE-He213
005 20220116205351.0
007 cr nn 008mamaa
008 101029s2010 xxu| s |||| 0|eng d
020 |a 9780387684413  |9 978-0-387-68441-3 
024 7 |a 10.1007/978-0-387-68441-3  |2 doi 
050 4 |a QA76.9.A43 
072 7 |a UMB  |2 bicssc 
072 7 |a COM051300  |2 bisacsh 
072 7 |a UMB  |2 thema 
082 0 4 |a 518.1  |2 23 
100 1 |a Downey, Rodney G.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Algorithmic Randomness and Complexity  |h [electronic resource] /  |c by Rodney G. Downey, Denis R. Hirschfeldt. 
250 |a 1st ed. 2010. 
264 1 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2010. 
300 |a XXVIII, 855 p. 8 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Theory and Applications of Computability, In cooperation with the association Computability in Europe,  |x 2190-6203 
505 0 |a Background -- Preliminaries -- Computability Theory -- Kolmogorov Complexity of Finite Strings -- Relating Complexities -- Effective Reals -- Notions of Randomness -- Martin-Löf Randomness -- Other Notions of Algorithmic Randomness -- Algorithmic Randomness and Turing Reducibility -- Relative Randomness -- Measures of Relative Randomness -- Complexity and Relative Randomness for 1-Random Sets -- Randomness-Theoretic Weakness -- Lowness and Triviality for Other Randomness Notions -- Algorithmic Dimension -- Further Topics -- Strong Jump Traceability -- ? as an Operator -- Complexity of Computably Enumerable Sets. 
520 |a Intuitively, a sequence such as 101010101010101010... does not seem random, whereas 101101011101010100..., obtained using coin tosses, does. How can we reconcile this intuition with the fact that both are statistically equally likely? What does it mean to say that an individual mathematical object such as a real number is random, or to say that one real is more random than another? And what is the relationship between randomness and computational power. The theory of algorithmic randomness uses tools from computability theory and algorithmic information theory to address questions such as these. Much of this theory can be seen as exploring the relationships between three fundamental concepts: relative computability, as measured by notions such as Turing reducibility; information content, as measured by notions such as Kolmogorov complexity; and randomness of individual objects, as first successfully defined by Martin-Löf. Although algorithmic randomness has been studied for several decades, a dramatic upsurge of interest in the area, starting in the late 1990s, has led to significant advances. This is the first comprehensive treatment of this important field, designed to be both a reference tool for experts and a guide for newcomers. It surveys a broad section of work in the area, and presents most of its major results and techniques in depth. Its organization is designed to guide the reader through this large body of work, providing context for its many concepts and theorems, discussing their significance, and highlighting their interactions. It includes a discussion of effective dimension, which allows us to assign concepts like Hausdorff dimension to individual reals, and a focused but detailed introduction to computability theory. It will be of interest to researchers and students in computability theory, algorithmic information theory, and theoretical computer science. 
650 0 |a Algorithms. 
650 0 |a Computer science. 
650 1 4 |a Algorithms. 
650 2 4 |a Theory of Computation. 
700 1 |a Hirschfeldt, Denis R.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9780387571850 
776 0 8 |i Printed edition:  |z 9780387955674 
776 0 8 |i Printed edition:  |z 9781493938209 
830 0 |a Theory and Applications of Computability, In cooperation with the association Computability in Europe,  |x 2190-6203 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-0-387-68441-3  |z Texto Completo 
912 |a ZDB-2-SMA 
912 |a ZDB-2-SXMS 
950 |a Mathematics and Statistics (SpringerNature-11649) 
950 |a Mathematics and Statistics (R0) (SpringerNature-43713)