Probabilistic Magnetotelluric Inversion with Adaptive Regularisation Using the No-U-Turns Sampler
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Backus, G. E. (1988). Bayesian inference in geomagnetism. Geophysical Journal International, 92(1), 125–142.
Betancourt, M. (2017). A conceptual introduction to hamiltonian monte carlo. arXiv:170102434
Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., et al. (2016). Stan: A probabilistic programming language. Journal of Statistical Software, 20, 1–37.
Chen, J., Hoversten, G. M., Key, K., Nordquist, G., & Cumming, W. (2012). Stochastic inversion of magnetotelluric data using a sharp boundary parameterization and application to a geothermal site. Geophysics, 77(4), E265–E279.
Chib, S., & Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The American Statistician, 49(4), 327–335.
Chong, A., Lam, K.P., Pozzi, M., & Yang, J. (2017). Bayesian calibration of building energy models with large datasets. Energy and Buildings
Creutz, M. (1988). Global monte carlo algorithms for many-fermion systems. Physical Review D, 38(4), 1228.
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science pp 457–472
Geman, S., & Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.
Grandis, H. (2006). Magnetotelluric (mt) inversion for 3-d conductivity model resolution using markov chain monte carlo (mcmc) algorithm: Preliminary results. Jurnal Geofisika, 1
Grandis, H., Menvielle, M., & Roussignol, M. (1999). Bayesian inversion with markov chainsi. The magnetotelluric one-dimensional case. Geophysical Journal International, 138(3), 757–768.
Guo, R., Dosso, S. E., Liu, J., Dettmer, J., & Tong, X. (2011). Non-linearity in bayesian 1-d magnetotelluric inversion. Geophysical Journal International, 185(2), 663–675.
Hoffman, M. D., & Gelman, A. (2014). The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research, 15(1), 1593–1623.
Krieger, L., & Peacock, J. R. (2014). Mtpy: A python toolbox for magnetotellurics. Computers & Geosciences, 72, 167–175.
Laloy, E., & Vrugt, J. A. (2012). High-dimensional posterior exploration of hydrologic models using multiple-try dream (zs) and high-performance computing. Water Resources Research, 48(1)
Mandolesi, E., Ogaya, X., Campanyà, J., & Agostinetti, N. P. (2018). A reversible-jump markov chain monte carlo algorithm for 1d inversion of magnetotelluric data. Computers & Geosciences, 113, 94–105.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.
Minsley, B. J. (2011). A trans-dimensional bayesian markov chain monte carlo algorithm for model assessment using frequency-domain electromagnetic data. Geophysical Journal International, 187(1), 252–272.
Monnahan, C. C., Thorson, J. T., & Branch, T. A. (2017). Faster estimation of bayesian models in ecology using hamiltonian monte carlo. Methods in Ecology and Evolution, 8(3), 339–348.
Nesterov, Y. (2009). Primal-dual subgradient methods for convex problems. Mathematical programming, 120(1), 221–259.
Rosas-Carbajal, M., Linde, N., Kalscheuer, T., & Vrugt, J. A. (2013). Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data. Geophysical Journal International, 196(3), 1508–1524.
Rosas-Carbajal, M., Linde, N., Peacock, J., Zyserman, F. I., Kalscheuer, T., & Thiel, S. (2015). Probabilistic 3-d time-lapse inversion of magnetotelluric data: Application to an enhanced geothermal system. Geophysical Supplements to the Monthly Notices of the Royal Astronomical Society, 203(3), 1946–1960.
Sanders, N. E., Soderberg, A. M., Gezari, S., Betancourt, M., Chornock, R., Berger, E., et al. (2015). Toward characterization of the type iip supernova progenitor population: A statistical sample of light curves from pan-starrs1. The Astrophysical Journal, 799(2), 208.
Schott, J. J., Roussignol, M., Menvielle, M., & Nomenjahanary, F. R. (1999). Bayesian inversion with markov chainsii. The one-dimensional dc multilayer case. Geophysical Journal International, 138(3), 769–783.
Shockley, E. M, Vrugt, J.A., & Lopez, C.F. (2017). Pydream: High-dimensional parameter inference for biological models in python. Bioinformatics
Tarits, P., Jouanne, V., Menvielle, M., & Roussignol, M. (1994). Bayesian statistics of non-linear inverse problems: Example of the magnetotelluric 1-d inverse problem. Geophysical Journal International, 119(2), 353–368.
Wait, J. R. (1962). Theory of magnetotelluric fields. Journal of Research NBS, 66(5), 509–541.