Product details:
| ISBN13: | 9781009098502 |
| ISBN10: | 10090985011 |
| Binding: | Hardback |
| No. of pages: | 346 pages |
| Size: | 262x185x29 mm |
| Weight: | 1060 g |
| Language: | English |
| 575 |
Category:
Data Modeling for the Sciences
Applications, Basics, Computations
Publisher: Cambridge University Press
Date of Publication: 31 August 2023
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Short description:
A self-contained and accessible guide to probabilistic data modeling, ideal for students and researchers in the natural sciences.
Long description:
With the increasing prevalence of big data and sparse data, and rapidly growing data-centric approaches to scientific research, students must develop effective data analysis skills at an early stage of their academic careers. This detailed guide to data modeling in the sciences is ideal for students and researchers keen to develop their understanding of probabilistic data modeling beyond the basics of p-values and fitting residuals. The textbook begins with basic probabilistic concepts, models of dynamical systems and likelihoods are then presented to build the foundation for Bayesian inference, Monte Carlo samplers and filtering. Modeling paradigms are then seamlessly developed, including mixture models, regression models, hidden Markov models, state-space models and Kalman filtering, continuous time processes and uniformization. The text is self-contained and includes practical examples and numerous exercises. This would be an excellent resource for courses on data analysis within the natural sciences, or as a reference text for self-study.
'Data Modeling for the Sciences, co-written by a mathematician and molecular scientist, manages to be rigorous, state-of-the-art, and yet accessible all at the same time. Experimentalists faced with complex data sets who need to take their data science to the next level will find this indispensable, and the book forms a great basis for a data science course in physics, chemistry, or biology departments.' Martin Gruebele, James R. Eiszner Chair, University of Illinois at Urbana-Champaign
'Data Modeling for the Sciences, co-written by a mathematician and molecular scientist, manages to be rigorous, state-of-the-art, and yet accessible all at the same time. Experimentalists faced with complex data sets who need to take their data science to the next level will find this indispensable, and the book forms a great basis for a data science course in physics, chemistry, or biology departments.' Martin Gruebele, James R. Eiszner Chair, University of Illinois at Urbana-Champaign
Table of Contents:
Part I. Concepts from Modeling, Inference, and Computing: 1. Probabilistic modeling and inference; 2. Dynamical systems and Markov processes; 3. Likelihoods and latent variables; 4. Bayesian inference; 5. Computational inference; Part II. Statistical Models: 6. Regression models; 7. Mixture models; 8. Hidden Markov models; 9. State-space models; 10. Continuous time models*; Part III. Appendix: Appendix A: Notation and other conventions; Appendix B: Numerical random variables; Appendix C: The Kronecker and Dirac deltas; Appendix D: Memoryless distributions; Appendix E: Foundational aspects of probabilistic modeling; Appendix F: Derivation of key relations; References; Index.
