Statistical Physics

An Advanced Approach with Applications

AvJosef Honerkamp

Häftad, Engelska, 2014

Del i serien Graduate Texts in Physics

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Beskrivning

The application of statistical methods to physics is essential. This unique book on statistical physics offers an advanced approach with numerous applications to the modern problems students are confronted with. Therefore the text contains more concepts and methods in statistics than the student would need for statistical mechanics alone.

Produktinformation

Utgivningsdatum:2014-07-18
Mått:155 x 235 x 34 mm
Vikt:848 g
Format:Häftad
Språk:Engelska
Serie:Graduate Texts in Physics
Antal sidor:554
Upplaga:3
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783642443855

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Mer om författaren

Prof. Dr. Josef Honerkamp Fakultät für Mathematik und Physik der Universität Freiburg

Recensioner i media

From the book reviews: "The book is carefully divided into two parts. The first part deals with modeling of statistical systems. The second part is devoted to the analysis of the respective systems. ... followed by a section that offers helpful hints and solutions to problems throughout the text, making it easier for students to deepen their understanding and confidence in their newfound knowledge. ... topics in each chapter are carefully selected and are well presented, making it a reliable reference for 'statistical physics.'" (Technometrics, Vol. 55 (2), May, 2013)

Innehållsförteckning

Statistical Physics is more than Statistical Mechanics.- Part I: Modeling of Statistical Systems.- Random Variables: Fundamentals of Probability Theory and Statistics.- Random Variables in State Space: Classical Statistical Mechanics of Fluids.- Random Fields: Textures and Classical Statistical Mechanics of Spin Systems.- Time-Dependent Random Variables: Classical Stochastic Processes.- Quantum Random Systems.- Changes of External Conditions.- Part II: Analysis of Statistical Systems.- Estimation of Parameters.- Signal Analysis: Estimation of Spectra.- Estimators Based on a Probability Distribution for the Parameters.- Identification of Stochastic Models from Observations.- Estimating the Parameters of a Hidden Stochastic Model.- Statistical Tests and Classification Methods.- Appendix: Random Number Generation for Simulating Realizations of Random Variables.- Problems.- Hints and Solutions.