Häftad, Spanska, 2012
187 kr
Skickas inom 5-8 vardagar
Fernando Soler Toscano – författare
Visar alla böcker från författaren . Handla med fri frakt och snabb leverans.
5 produkter
5 produkter
- Häftad, Engelska, 2011
546 kr
Skickas inom 10-15 vardagar
This book constitutes the proceedings of the Third International Congress on Tools for Teaching Logic, TICTTL 2011, held in Salamanca, Spain, in June 2011. The 30 papers presented were carefully reviewed and selected from 62 submissions. The congress focusses on a variety of topics including: logic teaching software, teaching formal methods, logic in the humanities, dissemination of logic courseware and logic textbooks, methods for teaching logic at different levels of instruction, presentation of postgraduate programs in logic, e-learning, logic games, teaching argumentation theory and informal logic, and pedagogy of logic. - E-bokPDF, Engelska, 2011
710 kr
Läs direkt efter köp
This book constitutes the proceedings of the Third International Congress on Tools for Teaching Logic, TICTTL 2011, held in Salamanca, Spain, in June 2011. The 30 papers presented were carefully reviewed and selected from 62 submissions. The congress focusses on a variety of topics including: logic teaching software, teaching formal methods, logic in the humanities, dissemination of logic courseware and logic textbooks, methods for teaching logic at different levels of instruction, presentation of postgraduate programs in logic, e-learning, logic games, teaching argumentation theory and informal logic, and pedagogy of logic. - Del 44 - Emergence, Complexity and Computation
Methods and Applications of Algorithmic Complexity
Beyond Statistical Lossless Compression
Inbunden, Engelska, 20221 839 kr
Skickas inom 10-15 vardagar
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 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. - E-bokEngelska, 2022
2 359 kr
Läs direkt efter köp
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 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.
