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020			_a9783662649855
024	7		_a10.1007/978-3-662-64985-5 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A43 _b2022 EB
100	1		_aZenil, Hector _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685494
245	1	0	_aMethods and Applications of Algorithmic Complexity : _bBeyond Statistical Lossless Compression _cby Hector Zenil, Fernando Soler Toscano, Nicolas Gauvrit
250			_a1st edition 2022
264		1	_aBerlin, Heidelberg _bSpringer International Publishing _c2022
300			_a1 recurso en línea (IX, 267 páginas) _b108 ilustraciones, 55 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aEmergence Complexity and Computation _x2194-7295 _v44
505	0		_aPreliminaries -- Enumerating and simulating Turing machines -- The Coding Theorem Method -- Theoretical aspects of finite approximations to Levin's semi-measure.
520			_aThis 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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9151535 _aEstructuras de datos (Informática)
650		7	_2embne _9151819 _aAlgoritmos computacionales
700	1		_aSoler Toscano, Fernando _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685495
700	1		_aGauvrit, Nicolas _d1972- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685496
776	0	8	_iPrinted edition: _z9783662649831
776	0	8	_iPrinted edition: _z9783662649848
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-64985-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eb _zSI