International Tables for Crystallography (2006). Vol. C, ch. 8.1, pp. 678-688
https://doi.org/10.1107/97809553602060000609

Chapter 8.1. Least squares

Chapter index

Bayes's theorem 8.1.2, 8.1.2
Best linear unbiased estimator 8.1.2
Broyden–Fletcher–Goldfarb–Shano update 8.1.4.3
Cholesky decomposition 8.1.3.2, 8.1.5.1
Cholesky factor 8.1.1.1, 8.1.3.1
Conditional probability density function 8.1.2
Conditioning 8.1.3.3, 8.1.3.3
Condition number 8.1.1.1, 8.1.3.3, 8.1.3.3
Conjugate-gradient methods 8.1.5.2
Cumulative distribution function 8.1.1.2
Davidon–Fletcher–Powell update 8.1.4.3
Eigenvalues 8.1.1.1
Eigenvectors 8.1.1.1
Estimates 8.1.2
Euclidean norm 8.1.1.1
Expectation values 8.1.1.2
Gauss–Markov theorem 8.1.2
Gauss–Newton algorithm 8.1.4.1
Givens rotations 8.1.1.1, 8.1.5.1, 8.1.5.1, 8.1.5.1
Hessian matrix 8.1.4.3, 8.1.4.3, 8.1.4.3, 8.1.4.3
Householder transformations 8.1.1.1, 8.1.1.1, 8.1.5.1
Induced matrix norm 8.1.1.1
Joint probability density function 8.1.1.2
Large-scale problems 8.1.5
Least-squares calculations 8.1
estimator 8.1.2, 8.1.2
nonlinear 8.1.4
software for 8.1.7
Levenberg–Marquardt algorithm 8.1.4.2
Linear algebra 8.1.1.1
Linear estimator 8.1.2
Marginal probability density function 8.1.1.2, 8.1.1.2
Mean 8.1.1.2
Non-linear least squares 8.1.4
Normal equations 8.1.2, 8.1.2, 8.1.3.2
Numerical methods 8.1.5
Posterior probability density function 8.1.2
Prior probability density function 8.1.2
Probability density function 8.1.1.2
Quasi-Newton methods 8.1.4.3, 8.1.4.3
Refinement
of structural parameters 8.1
Secant methods 8.1.4.3
Sparse matrices 8.1.5.1
Standard deviation 8.1.2
Standard uncertainty 8.1.2
Statistics 8.1, 8.1.1.2
Stopping rules 8.1.4.4, 8.1.4.4
Trust-region methods 8.1.4.2
Variance 8.1.1.2
Variance–covariance matrices 8.1.1.2, 8.1.1.2, 8.1.2, 8.1.2