An implementation of the randomize-then-optimize Monte Carlo sampling algorithm for Gaussian priors [1] and l1 priors [2]. This implementation is strongly influenced by Marko Laine's Matlab implementation [3].
Run any of the python files in /test to see how it works, e.g.
python3 -i rto_BOD_l2.py
[1]: Bardsley, Johnathan M., et al. "Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems." SIAM Journal on Scientific Computing 36.4 (2014): A1895-A1910.
[2]: Wang, Zheng, et al. "Bayesian Inverse Problems with l_1 Priors: A Randomize-Then-Optimize Approach." SIAM Journal on Scientific Computing 39.5 (2017): S140-S166.
[3]: http://helios.fmi.fi/~lainema/rto/
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