7.2.2 MAP Estimates
7.2.3 Sampling： Gibbs Sampler
7.3 Biomagnetic Inverse Problem： Source Localization
7.3.1 Reconstruction with Gaussian White Noise Prior Model
7.3.2 Reconstruction of Dipole Strengths with the e1-prior Model
7.4 Dynamic MEG by Bayes Filtering
7.4.1 A Single Dipole Model
7.4.2 More Realistic Geometry
7.4.3 Multiple Dipole Models
7.5 Electrical Impedance Tomography： Optimal Current Patterns
7.5.1 A Posteriori Synthesized Current Patterns
7.5.2 Optimization Criterion
7.5.3 Numerical Examples
7.6 Electrical Impedance Tomography： Handling Approximation Errors
7.6.1 Meshes and Projectors
7.6.2 The Prior Distribution and the Prior Model
7.6.3 The Enhanced Error Model
7.6.4 The MAP Estimates
7.7 Electrical Impedance Process Tomography
7.7.1 The Evolution Model
7.7.2 The Observation Model and the Computational Scheme
7.7.3 The Fixed-lag State Estimate
7.7.4 Estimation of the Flow Profile
7.8 Optical Tomography in Anisotropic Media
7.8.1 The Anisotropy Model
7.8.2 Linearized Model
7.9 Optical Tomography： Boundary Recovery
7.9.1 The General Elliptic Case
7.9.2 Application to Optical Diffusion Tomography
7.10 Notes and Comments

A Appendix： Linear Algebra and Functional Analysis
A.1 Linear Algebra
A.2 Functional Analysis
A.3 Sobolev Spaces
B Appendix 2： Basics on Probability
B.1 Basic Concepts
B.2 Conditional Probabilities
References
Index

 2/2   首页 上一页 1 2

自然科学 数学 概率论与数理统计

在线阅读