Steve Presse, Ioannis Sgouralis

Data Modeling for the Sciences (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 31. August 2023
• Englisch
• ISBN-13: 9781009115445
• Artikelnr.: 69002810

Autorenporträt

Steve Pressé is Professor of Physics and Chemistry at Arizona State University, Tempe. His research lies at the interface of Biophysics and Chemical Physics with an emphasis on inverse methods. He is a recipient of a National Science Foundation CAREER award and a Research Corporation 'Molecules come to Life' Fellow. He has extensive experience in teaching data analysis and modeling at both undergraduate and graduate level with funding from the NIH and NSF in data modelling applied to the interpretation of single molecule dynamics and image analysis.

Inhaltsangabe

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.