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A unique text at the advanced level with applications to modern physics
Three new chapters
Web-enhanced problems and solutions
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. Methods from mathematical statistics and stochastics for the analysis of data are discussed as well. The book is divided into two parts, focusing first on the modeling of statistical systems and then on the analysis of these systems. Problems with hints for solution help the students to deepen their knowledge. The second edition has been updated and enlarged with new material on estimators based on a probability distribution for the parameters, identification of stochastic models from observations, and statistical tests and classification methods (Chaps. 10-12). Moreover, a customized set of problems with solutions is accessible on the Web. The author teaches and conducts research on stochastic dynamical systems at the University of Freiburg, Germany.
Content Level »Graduate
Keywords »linear optimization - probability theory - random variables - statistical physics - statistical tests - statistics - stochastic models - stochastic processes
1 Statistical Physics: Is More than Statistical Mechanics.- I Modeling of Statistical Systems.- 2 Random Variables: Fundamentals of Probability Theory and Statistics.- 3 Random Variables in State Space: Classical Statistical Mechanics of Fluids.- 4 Random Fields: Textures and Classical Statistical Mechanics of Spin Systems.- 5 Time-Dependent Random Variables: Classical Stochastic Processes.- 6 Quantum Random Systems.- 7 Changes of External Conditions.- II Analysis of Statistical Systems.- 8 Estimation of Parameters.- 9 Signal Analysis: Estimation of Spectra.- 10 Estimators Based on a Probability Distribution for the Parameters.- 11 Identification of Stochastic Models from Observations.- 12 Estimating the Parameters of a Hidden Stochastic Model.- 13 Statistical Tests and Classification Methods.- Appendix: Random Number Generation for Simulating Realizations of Random Variables.- Problems.- Hints and Solutions.- References.