Data Modeling for the Sciences

ISBN-13: 9781009098502 / Twarda / 2023 / 346 str.

Knoxville) Sgouralis Ioannis (University of Tennessee

Opis

A self-contained and accessible guide to probabilistic data modeling, ideal for students and researchers in the natural sciences.

Recenzje

'This impressive mathematical treatise lays out a rigorous approach for data analysis and modeling of complex physical systems based on a modern data-centric approach, where noisy measurements are used to extract models for stochastic behavior. Presse and Sgouralis are to be congratulated on the breadth and depth of their presentation.' W. E. Moerner, Stanford University

Spis treści

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.