Probabilistic Data-Driven Modeling

Auteur

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.

Texte du rabat

This book introduces a powerful selection of methodologies and approaches for constructing models from data from various domains from statistics to complexity science. This book uses the estimation of multivariate probabilities as a frame of reference for the entire domain, from linear regressions to deep learning neural networks.

Contenu

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.