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 third edition has been updated and enlarged with new sections deepening the knowledge about data analysis. Moreover, a customized set of problems with solutions is accessible on the Web at extras.springer.com.

Focuses on concepts and methods in statistics, giving detailed insight in analyzing physical data Provides an thorough introduction to statistical physics with an emphasis on the analysis of statistical systems Includes advanced topics, reference lists, problems with solutions and boxed inserts, making it an invaluable source for students, even after completion of the course The introduction to filters, wavelets and many practical and concrete algorithms close a noticeable gap left open by other textbooks Third edition offers 50 pages of new material, especially on data-analysis, Gillespie-algorithm, Kaiman filters, plus new problems with solutions Covers all modern applications of statistics Includes supplementary material: sn.pub/extras

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

Inhalt

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.