BibliographyΒΆ

Hansen, T. M., Cordua, K. S., & Mosegaard, K. (2012). Inverse problems with non-trivial priors: Efficient solution through sequential Gibbs sampling. Computational Geosciences, 16(3), 593-611.
Hansen, T. M., Cordua, K. S., Looms, M. C., & Mosegaard, K. (2013a). SIPPI: A Matlab toolbox for sampling the solution to inverse problems with complex prior information: Part 1 β€” Methodology. Computers & Geosciences, 52, 470-480.
Hansen, T. M., Cordua, K. S., Looms, M. C., & Mosegaard, K. (2013b). SIPPI: A Matlab toolbox for sampling the solution to inverse problems with complex prior information: Part 2 β€” Application to crosshole GPR tomography. Computers & Geosciences, 52, 481-492.
Hansen, T.M., Cordua. K.S., and Mosegaard, K. (2015). A general probabilistic approach for inference of Gaussian model parameters from noisy data of point and volume support. Mathematical Geosciences 47(7), pp 843-865.
Hansen, T.M., Cordua, K. S., Jacobsen, B. J., and Mosegaard, K. (2015). Accounting for imperfect forward modeling in geophysical inverse problems - exemplified for cross hole tomography.
Geophsyics, 79(3) H1-H21, 2014.
Hansen, T. M., Vu, L. T., and Bach. T. (2016). MPSLIB: A C++ class for sequential simulation of multiple-point statistical models. Software X, vol 5, pp 127–133.
Sambridge, M. (2014). A parallel tempering algorithm for probabilistic sampling and multimodal optimization. Geophysical Journal International 196(1).
Tarantola, A., and Valette, B. (1982). Inverse problems= quest for information. J. geophys 50(3), 150-170.
Tarantola, A. (2005). Inverse problem theory and methods for model
parameter estimation. SIAM.