A python implementation of Bayesian adaptive spline surfaces (BASS). Similar to Bayesian multivariate adaptive regression splines (Bayesian MARS) introduced in Denison et al. (1998).
Use
pip install git+https://github.com/lanl/pyBASS.git-
Friedman, J.H., 1991. Multivariate adaptive regression splines. The annals of statistics, pp.1-67.
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Denison, D.G., Mallick, B.K. and Smith, A.F., 1998. Bayesian MARS. Statistics and Computing, 8(4), pp.337-346.
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Francom, D., Sansó, B., Kupresanin, A. and Johannesson, G., 2018. Sensitivity analysis and emulation for functional data using Bayesian adaptive splines. Statistica Sinica, pp.791-816.
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Francom, D., Sansó, B., Bulaevskaya, V., Lucas, D. and Simpson, M., 2019. Inferring atmospheric release characteristics in a large computer experiment using Bayesian adaptive splines. Journal of the American Statistical Association, 114(528), pp.1450-1465.
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Francom, D. and Sansó, B., 2020. BASS: An R package for fitting and performing sensitivity analysis of Bayesian adaptive spline surfaces. Journal of Statistical Software, 94(1), pp.1-36.
Copyright 2020. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.
LANL software release C19112
Author: Devin Francom