國立虎尾科技大學 |

Methods and Applications of Algorithmic Complexity = Beyond Statistical Lossless Compression /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Methods and Applications of Algorithmic Complexity/ by Hector Zenil, Fernando Soler Toscano, Nicolas Gauvrit.
其他題名:	Beyond Statistical Lossless Compression /
作者:	Zenil, Hector.
其他作者:	Toscano, Fernando Soler.
面頁冊數:	IX, 267 p. 108 illus., 55 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Dynamics. -
電子資源:	https://doi.org/10.1007/978-3-662-64985-5
ISBN:	9783662649855

Methods and Applications of Algorithmic Complexity = Beyond Statistical Lossless Compression /
Zenil, Hector.

Methods and Applications of Algorithmic ComplexityBeyond Statistical Lossless Compression /[electronic resource] :by Hector Zenil, Fernando Soler Toscano, Nicolas Gauvrit. - 1st ed. 2022. - IX, 267 p. 108 illus., 55 illus. in color.online resource. - Emergence, Complexity and Computation,442194-7295 ;. - Emergence, Complexity and Computation,12.

Preliminaries -- Enumerating and simulating Turing machines -- The Coding Theorem Method -- Theoretical aspects of ﬁnite approximations to Levin’s semi-measure.

This book explores a different pragmatic approach to algorithmic complexity rooted or motivated by the theoretical foundations of algorithmic probability and explores the relaxation of necessary and sufficient conditions in the pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance and applicability. Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently coexist for the first time, ranging from the dominant ones based upon popular statistical lossless compression algorithms (such as LZW) to newer approaches that advance, complement, and also pose their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented, and despite their many challenges, some of these methods are better grounded in or motivated by the principles of algorithmic information. The authors propose that the field can make greater contributions to science, causation, scientific discovery, networks, and cognition, to mention a few among many fields, instead of remaining either as a technical curiosity of mathematical interest only or as a statistical tool when collapsed into an application of popular lossless compression algorithms. This book goes, thus, beyond popular statistical lossless compression and introduces a different methodological approach to dealing with algorithmic complexity. For example, graph theory and network science are classic subjects in mathematics widely investigated in the twentieth century, transforming research in many fields of science from economy to medicine. However, it has become increasingly clear that the challenge of analyzing these networks cannot be addressed by tools relying solely on statistical methods. Therefore, model-driven approaches are needed. Recent advances in network science suggest that algorithmic information theory could play an increasingly important role in breaking those limits imposed by traditional statistical analysis (entropy or statistical compression) in modeling evolving complex networks or interacting networks. Further progress on this front calls for new techniques for an improved mechanistic understanding of complex systems, thereby calling out for increased interaction between systems science, network theory, and algorithmic information theory, to which this book contributes.

ISBN: 9783662649855

Standard No.: 10.1007/978-3-662-64985-5doiSubjects--Topical Terms:

592238
Dynamics.

LC Class. No.: TA352-356

Dewey Class. No.: 515.39

Methods and Applications of Algorithmic Complexity = Beyond Statistical Lossless Compression /
LDR:04145nam a22004335i 4500 001 1089619
003 DE-He213
005 20220516164812.0
007 cr nn 008mamaa
008 221228s2022 gw | s |||| 0|eng d
020 $a 9783662649855 $9 978-3-662-64985-5
024 7 $a 10.1007/978-3-662-64985-5 $2 doi
035 $a 978-3-662-64985-5
050 4 $a TA352-356
050 4 $a QC20.7.N6
072 7 $a TBJ $2 bicssc
072 7 $a GPFC $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a TBJ $2 thema
072 7 $a GPFC $2 thema
082 0 4 $a 515.39 $2 23
100 1 $a Zenil, Hector. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 832996
245 1 0 $a Methods and Applications of Algorithmic Complexity $h [electronic resource] : $b Beyond Statistical Lossless Compression / $c by Hector Zenil, Fernando Soler Toscano, Nicolas Gauvrit.
250 $a 1st ed. 2022.
264 1 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2022.
300 $a IX, 267 p. 108 illus., 55 illus. in color. $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 Emergence, Complexity and Computation, $x 2194-7295 ; $v 44
505 0 $a Preliminaries -- Enumerating and simulating Turing machines -- The Coding Theorem Method -- Theoretical aspects of ﬁnite approximations to Levin’s semi-measure.
520 $a This book explores a different pragmatic approach to algorithmic complexity rooted or motivated by the theoretical foundations of algorithmic probability and explores the relaxation of necessary and sufficient conditions in the pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance and applicability. Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently coexist for the first time, ranging from the dominant ones based upon popular statistical lossless compression algorithms (such as LZW) to newer approaches that advance, complement, and also pose their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented, and despite their many challenges, some of these methods are better grounded in or motivated by the principles of algorithmic information. The authors propose that the field can make greater contributions to science, causation, scientific discovery, networks, and cognition, to mention a few among many fields, instead of remaining either as a technical curiosity of mathematical interest only or as a statistical tool when collapsed into an application of popular lossless compression algorithms. This book goes, thus, beyond popular statistical lossless compression and introduces a different methodological approach to dealing with algorithmic complexity. For example, graph theory and network science are classic subjects in mathematics widely investigated in the twentieth century, transforming research in many fields of science from economy to medicine. However, it has become increasingly clear that the challenge of analyzing these networks cannot be addressed by tools relying solely on statistical methods. Therefore, model-driven approaches are needed. Recent advances in network science suggest that algorithmic information theory could play an increasingly important role in breaking those limits imposed by traditional statistical analysis (entropy or statistical compression) in modeling evolving complex networks or interacting networks. Further progress on this front calls for new techniques for an improved mechanistic understanding of complex systems, thereby calling out for increased interaction between systems science, network theory, and algorithmic information theory, to which this book contributes.
650 0 $a Dynamics. $3 592238
650 0 $a Nonlinear theories. $3 527937
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Applied Dynamical Systems. $3 1366186
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Toscano, Fernando Soler. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1396899
700 1 $a Gauvrit, Nicolas. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1396900
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783662649831
776 0 8 $i Printed edition: $z 9783662649848
830 0 $a Emergence, Complexity and Computation, $x 2194-7287 ; $v 12 $3 1254548
856 4 0 $u https://doi.org/10.1007/978-3-662-64985-5
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)