Methods applications algorithmic complexity par zenil hector (13 résultats)
Langue : anglais
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Langue : anglais
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- Couverture rigide
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Langue : anglais
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Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
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Hardcover. Etat : new. Hardcover. 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 en…tailing 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 informationtheory 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Langue : anglais
Edité par Springer, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
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Langue : anglais
Edité par Springer, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
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Etat : New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Langue : anglais
Edité par Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
- Édition originale
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Hardcover. Etat : new. Hardcover. 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 en…tailing 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 informationtheory 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Methods and Applications of Algorithmic Complexity: Beyond Statistical Lossless Compression
Zenil, Hector (Author)/ Toscano, Fernando Soler (Author)/ Gauvrit, Nicolas (Author)
Langue : anglais
Edité par Springer, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
- impression à la demande
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Hardcover. Etat : Brand New. 276 pages. 9.25x6.10x0.69 inches. In Stock. This item is printed on demand.
Langue : anglais
Edité par Springer, Berlin|Springer Berlin Heidelberg|Springer, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
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Vendeur : moluna, Greven, Allemagnemoluna
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 c…onditions in the pursuit of numerical appli.
Langue : anglais
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Mai 2022, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
- impression à la demande
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 nume…rical 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 informationtheory 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. 280 pp. Englisch.
Langue : anglais
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Mai 2022, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
- impression à la demande
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagnebuchversandmimpf2000
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 numerica…l 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 informationtheory 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 280 pp. Englisch.
Langue : anglais
Edité par Springer, 2022
Série : Livre 42 sur 47 - Emergence, Complexity and Computation
- Couverture rigide
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Langue : anglais
Edité par Springer, 2022
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- Couverture rigide
- impression à la demande
Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
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