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xx Contents

A.1.3 QR factorization ............................ 649
A.1.4 Cholesky factorization ......................... 650
A.2 Linear least squares ............................... 651
A.2.1 Total least squares ........................... 653
A.3 Non-linear least squares ............................. 654
A.4 Direct sparse matrix techniques ......................... 655
A.4.1 Variable reordering ........................... 656
A.5 Iterative techniques ............................... 656
A.5.1 Conjugate gradient ........................... 657
A.5.2 Preconditioning ............................. 659
A.5.3 Multigrid ................................ 660
B Bayesian modeling and inference 661
B.1 Estimation theory ................................ 662
B.1.1 Likelihood for multivariate Gaussian noise .............. 663
B.2 Maximum likelihood estimation and least squares ............... 665
B.3 Robust statistics ................................. 666
B.4 Prior models and Bayesian inference ...................... 667
B.5 Markov random fields .............................. 668
B.5.1 Gradient descent and simulated annealing ............... 670
B.5.2 Dynamic programming ......................... 670
B.5.3 Belief propagation ........................... 672
B.5.4 Graph cuts ............................... 674
B.5.5 Linear programming .......................... 676
B.6 Uncertainty estimation (error analysis) ..................... 678

C Supplementary material 679
C.1 Data sets ..................................... 680
C.2 Software ..................................... 682
C.3 Slides and lectures ............................... 689
C.4 Bibliography .................................. 690
References 691

Index 793