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There is a considerable amount of interesting discussion on inference generally and, in particular, on Bayesian inference. While a statistician might find the language and point-of-view somewhat different, this is a useful resource for those curious about the use of statistics in modern physics.
Thought-provoking and challenging.
The value of a statistical method outlined by [the Rev. Thomas] Bayes in 1763 has become increasingly apparent and has resulted in a blossoming of 'Bayesian' methods in scientific fields ranging from archeology to computing.
Very well written and well organized. The approach is original and deserves to be widely known. This is an excellent book.
Lemm's approach is original and very fruitful, and deserves to be widely known. Even for the insider the book is a source of new ideas and applications. The last chapter on inverse quantum theory is expected to be of great importance in the growing fields of quantum information processing and atomic force spectroscopy.
About
Book Details
Publication Date
Status
Available
Trim Size
6.125
x 9.25
Pages
432
ISBN
9780801877971
Table of Contents
List of Figures
List of Tables
List of Numerical Case Studies
Acknowledgments
Part I: Introduction
Part II: Bayesian Framework
Chapter 1. Bayesian Models
Chapter 2. Bayesian Decision Theory
Chapter 3. A
List of Figures
List of Tables
List of Numerical Case Studies
Acknowledgments
Part I: Introduction
Part II: Bayesian Framework
Chapter 1. Bayesian Models
Chapter 2. Bayesian Decision Theory
Chapter 3. A Priori Information
Part III: Gaussian Prior Factors
Chapter 4. Gaussian Prior Factor for Log-Likelihoods
Chapter 5. Gaussian Prior Factor For Likelihoods
Chapter 6. Quadratic Density Estimation and Empirical Risk Minimization
Chapter 7. Numerical Case Study: Density Estimation with Gaussian Specific Priors
Chapter 8. Gaussian Prior Factors for General Fields
Chapter 9. Covariances and Invariances
Chapter 10. Non-Zero Means
Chapter 11. Regression
Chapter 12. Classification
Part IV: Parameterizing Likelihoods: Variational Methods
Chapter 13. General Likelihood Parameterizations
Chapter 14. Gaussian Priors for Likelihood Parameterizations
Chapter 15. Linear Trial Spaces
Chapter 16. Linear Regression
Chapter 17. Mixture Models
Chapter 18. Additive Models
Chapter 19. Product Ansatz
Chapter 20. Decision Trees
Chapter 21. Projection Pursuit
Chapter 22. Neural Networks
Part V: Parameterizing Priors: Hyperparameters
Chapter 23. Quenched and Annealed Prior Normalization
Chapter 24. Saddle Point Approximations and Hyperparameters
Chapter 25. Adapting Prior Means
Chapter 26. Adapting Prior Covariances
Chapter 27. Integer Hyperparameters
Chapter 28. Hyperfields
Chapter 29. Auxiliary Fields
Chapter 30. Non-Quadratic Potentials
Part VI: Mixtures of Gaussian Prior Factors
Chapter 31. Multimodal Energy Surfaces
Chapter 32. Prior Mixtures for Density Estimation
Chapter 33. Numerical Case Study: Prior Mixtures for Density Estimation
Chapter 34. Prior Mixtures for Regression
Chapter 35. Local Mixtures
Chapter 36. Numerical Case Study: Image Completion
Part VII: Bayesian Inverse Quantum Theory (BIQT)
Chapter 37. Bayesian Inverse Quantum Statistics (BIQS)
Chapter 38. Bayesian Inverse Time-Dependent Quantum Theory (BITDQ)
Chapter 39. Bayesian Inverse Many-Body Theory
Part VIII: Summary
Bibliography
Index
