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Full Description
Based on the long-running Probability Theory course at the Sapienza University of Rome, this book offers a fresh and in-depth approach to probability and statistics, while remaining intuitive and accessible in style. The fundamentals of probability theory are elegantly presented, supported by numerous examples and illustrations, and modern applications are later introduced giving readers an appreciation of current research topics. The text covers distribution functions, statistical inference and data analysis, and more advanced methods including Markov chains and Poisson processes, widely used in dynamical systems and data science research. The concluding section, 'Entropy, Probability and Statistical Mechanics' unites key concepts from the text with the authors' impressive research experience, to provide a clear illustration of these powerful statistical tools in action. Ideal for students and researchers in the quantitative sciences this book provides an authoritative account of probability theory, written by leading researchers in the field.
Contents
1. Introduction to probability; 2. Probability distributions; 3. Law of large numbers and central limit theorem; 4. Large deviations; 5. Statistical inference and experimental data analysis; 6. Multivariate and correlated experimental data; 7. Random walkers; 8. Generating functions and chain reactions; 9. Recurrent events; 10. Markov chains; 11. Numerical simulations; 12. Correlated events; 13. Continuous time Markov processes; 14. Entropy, Probability, Statistical Mechanics.
