Bayesian Probability Theory

Bayesian Probability Theory : Applications in the Physical Sciences

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Description

From the basics to the forefront of modern research, this book presents all aspects of probability theory, statistics and data analysis from a Bayesian perspective for physicists and engineers. The book presents the roots, applications and numerical implementation of probability theory, and covers advanced topics such as maximum entropy distributions, stochastic processes, parameter estimation, model selection, hypothesis testing and experimental design. In addition, it explores state-of-the art numerical techniques required to solve demanding real-world problems. The book is ideal for students and researchers in physical sciences and engineering.show more

Product details

  • Electronic book text
  • CAMBRIDGE UNIVERSITY PRESS
  • Cambridge University Press (Virtual Publishing)
  • Cambridge, United Kingdom
  • 128 b/w illus.
  • 1139950347
  • 9781139950343

About Wolfgang von der Linden

Wolfgang von der Linden is Professor for Theoretical and Computational Physics at the Graz University of Technology. His research area is statistical physics with focus on strongly correlated quantum-many-body physics, based on computational techniques. Volker Dose is a former Director of the surface physics division of the Max Planck Institute for plasma physics. He has contributed to Bayesian methods in physics, astronomy and climate research. Udo von Toussaint is a Senior Scientist in the material research division of the Max Planck Institute for plasma physics, where he works on Bayesian experimental design, data fusion, molecular dynamics and inverse problems in the field of plasma-wall interactions.show more

Table of contents

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.show more

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