Probabilistic Data-Driven Modeling
portes grátis
Probabilistic Data-Driven Modeling
Aste, Tomaso
Cambridge University Press
10/2024
420
Dura
9781009221856
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
