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＜E-Book＞
Generalised Thermostatistics / by Jan Naudts

Edition	1st ed. 2011.
Publisher	(London : Springer London : Imprint: Springer)
Year	2011
Language	English
Size	XI, 201 p : online resource
Authors	*Naudts, Jan author SpringerLink (Online service)
Subjects	LCSH:Mathematics LCSH:System theory LCSH:Mathematical physics LCSH:Thermodynamics FREE:Mathematics FREE:Complex Systems FREE:Mathematical Methods in Physics FREE:Thermodynamics FREE:Theoretical, Mathematical and Computational Physics
Notes	Parameter estimation -- Statistical Models -- Thermodynamic Equilibrium -- The Microcanonical Ensemble -- Hyperensembles -- The Mean Field Approximation -- q-Deformed Distributions -- Tsallis’ Thermostatistics -- Changes of Scale -- General deformations -- General Entropies The domain of non-extensive thermostatistics has been subject to intensive research over the past twenty years and has matured significantly. Generalised Thermostatistics cuts through the traditionalism of many statistical physics texts by offering a fresh perspective and seeking to remove elements of doubt and confusion surrounding the area. The book is divided into two parts - the first covering topics from conventional statistical physics, whilst adopting the perspective that statistical physics is statistics applied to physics. The second developing the formalism of non-extensive thermostatistics, of which the central role is played by the notion of a deformed exponential family of probability distributions. Presented in a clear, consistent, and deductive manner, the book focuses on theory, part of which is developed by the author himself, but also provides a number of references towards application-based texts. Written by a leading contributor in the field, this book will provide a useful tool for learning about recent developments in generalized versions of statistical mechanics and thermodynamics, especially with respect to self-study. Written for researchers in theoretical physics, mathematics and statistical mechanics, as well as graduates of physics, mathematics or engineering. A prerequisite knowledge of elementary notions of statistical physics and a substantial mathematical background are required HTTP:URL=https://doi.org/10.1007/978-0-85729-355-8

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