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.
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.
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.
Inhaltsangabe
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.
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.
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