Probability Theory for Quantitative Scientists

This book provides an authoritative account of probability theory for quantitative scientists, written by leading researchers in the field.

Luca Leuzzi (Author), Enzo Marinari (Author), Giorgio Parisi (Author)

9781009580694, Cambridge University Press

Hardback, published 14 August 2025

432 pages
25.9 x 18.3 x 2.5 cm, 1.05 kg

'Here, at last, is a book that strikes the right balance between a solid presentation of probability theory and examples of advanced developments in various fields of science…this is a must read for anyone who wants to understand the use of probability in data analysis, statistical inference, or stochastic processes, as well as its deep connections to statistical physics.' Marc Mézard, Bocconi University

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.

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.

Subject Areas: Applied physics [PHV]