Probabilistic Data-Driven Modeling

Inbunden, Engelska, 2025

Av Tomaso Aste, Tomaso (University College London) Aste

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This book introduces relevant and established data-driven modeling tools currently in use or in development, which will help readers master the art and science of constructing models from data and dive into different application areas. It presents statistical tools useful to individuate regularities, discover patterns and laws in complex datasets, and demonstrates how to apply them to devise models that help to understand these systems and predict their behaviors. By focusing on the estimation of multivariate probabilities, the book shows that the entire domain, from linear regressions to deep learning neural networks, can be formulated in probabilistic terms. This book provides the right balance between accessibility and mathematical rigor for applied data science or operations research students, graduate students in CSE, and machine learning and uncertainty quantification researchers who use statistics in their field. Background in probability theory and undergraduate mathematics is assumed.

Produktinformation

Utgivningsdatum2025-05-01
Mått182 x 260 x 35 mm
Vikt1 000 g
FormatInbunden
SpråkEngelska
Antal sidor452
FörlagCambridge University Press
ISBN9781009221856

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Tomaso Aste is Professor of Complexity Science at the Computer Science Department at University College London. A trained physicist, he has substantially contributed to research in complex systems modeling, from materials to markets. He has authored over 300 research papers and several books and collaborated with over 100 researchers across all continents. Professor Aste is founder and Head of the Financial Computing and Analytics Group at UCL, he is founder and editor-in-chief of the journal Data-Driven Modelling.

List of symbols; Preface; Part I. Preliminary: 1. Introduction; 2. Fundamentals of Probability; 3. Fundamentals of machine learning; 4. Fundamentals of networks; Part II. Foundations of Probabilistic Modeling: 5. Univariate probabilities; 6. Multivariate probabilities; 7. Entropies; 8. Dependence; 9. Stochastic processes and scaling laws; 10. Causation; 11. Networks as representations of complex systems; 12. Probabilistic modeling with network representations; Part III. Model Construction from Data: 13. Nonparametric estimation of univariate probabilities from data; 14. Parametric estimation of univariate probabilities from data; 15. Estimation of multivariate probabilities from data; 16. Time series and probabilistic modeling; 17. Construction of network representations from data; 18. Assessing the goodness of models; Part IV. Closing: 19. Conclusions; Part V. Appendices: Appendix A. Essentials on probability theory; Appendix B. Finding roots of non-linear equations; Appendix C. Some optimization problems and methods; Appendix D. Principal components analysis; Appendix E. Random forest; Appendix F. Expectation maximization (EM); Appendix G. Bad modeling; References; Index.