{"product_id":"probabilistic-data-driven-modeling-hardback-9781009221856","title":"Probabilistic Data-Driven Modeling (Hardback) 9781009221856","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eProbabilistic Data-Driven Modeling\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eA probabilistic data-driven modeling toolbox to help students and researchers characterize, classify and model real complex systems.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eTomaso Aste (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781009221856, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 1 May 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e452 pages\u003cbr\u003e26 x 18.2 x 3.5 cm, 1 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'This is a much-needed book that comprehensively reviews data analysis concepts and methods for complex systems.It starts with probability and statistics and clearly and succinctly connects it to information theory and network analysis. I look forward to having it on my shelf, and I will recommend it to all my students!' J. Doyne Farmer, University of Oxford\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eList of symbols\u003cbr\u003e Preface\u003cbr\u003e Part I. Preliminary: 1. Introduction\u003cbr\u003e 2. Fundamentals of Probability\u003cbr\u003e 3. Fundamentals of machine learning\u003cbr\u003e 4. Fundamentals of networks\u003cbr\u003e Part II. Foundations of Probabilistic Modeling: 5. Univariate probabilities\u003cbr\u003e 6. Multivariate probabilities\u003cbr\u003e 7. Entropies\u003cbr\u003e 8. Dependence\u003cbr\u003e 9. Stochastic processes and scaling laws\u003cbr\u003e 10. Causation\u003cbr\u003e 11. Networks as representations of complex systems\u003cbr\u003e 12. Probabilistic modeling with network representations\u003cbr\u003e Part III. Model Construction from Data: 13. Nonparametric estimation of univariate probabilities from data\u003cbr\u003e 14. Parametric estimation of univariate probabilities from data\u003cbr\u003e 15. Estimation of multivariate probabilities from data\u003cbr\u003e 16. Time series and probabilistic modeling\u003cbr\u003e 17. Construction of network representations from data\u003cbr\u003e 18. Assessing the goodness of models\u003cbr\u003e Part IV. Closing: 19. Conclusions\u003cbr\u003e Part V. Appendices: Appendix A. Essentials on probability theory\u003cbr\u003e Appendix B. Finding roots of non-linear equations\u003cbr\u003e Appendix C. Some optimization problems and methods\u003cbr\u003e Appendix D. Principal components analysis\u003cbr\u003e Appendix E. Random forest\u003cbr\u003e Appendix F. Expectation maximization (EM)\u003cbr\u003e Appendix G. Bad modeling\u003cbr\u003e References\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Numerical analysis [\u003ca title=\"See our other books on Numerical analysis\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Numerical%20analysis%20%5BPBKS%5D%22\"\u003ePBKS\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Brand New","offer_id":52460670877976,"sku":"9781009221856","price":44.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781009221856i.jpg?v=1785457695","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/probabilistic-data-driven-modeling-hardback-9781009221856","provider":"Freshly Printed Books","version":"1.0","type":"link"}