This accessible guide to data modeling introduces basic probabilistic concepts, gradually building toward state-of-the art data modeling and analysis techniques. Aimed at students and researchers in the sciences, the text is self-contained and pedagogical, including practical examples and end of chapter problems.
This accessible guide to data modeling introduces basic probabilistic concepts, gradually building toward state-of-the art data modeling and analysis techniques. Aimed at students and researchers in the sciences, the text is self-contained and pedagogical, including practical examples and end of chapter problems.
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.
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.
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