Bayesian Analysis of Constrained Gaussian Processes
Author:
Affiliation:
1. CY Tech, CY Cergy Paris Université, Laboratoire AGM, Site du Parc, 95011 Cergy-Pontoise, France
2. Mines Saint-Étienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Institut Henri Fayol, F-42023 Saint-Étienne France
Publisher
Institute of Mathematical Statistics
Reference48 articles.
1. Lin, L. and Dunson, D. B. (2014). “Bayesian monotone regression using Gaussian process projection.” Biometrika, 101(2): 303–317.
2. Neelon, B. and Dunson, D. B. (2004). “Bayesian isotonic regression and trend analysis.” Biometrics, 60(2): 398–406.
3. Pakman, A. and Paninski, L. (2014). “Exact Hamiltonian Monte Carlo for truncated multivariate Gaussians.” Journal of Computational and Graphical Statistics, 23(2): 518–542.
4. Hampel, F. R. (1974). “The influence curve and its role in robust estimation.” Journal of the American Statistical Association, 69(346): 383–393.
5. Curtis, S. M. and Ghosh, S. K. (2011). “A variable selection approach to monotonic regression with Bernstein polynomials.” Journal of Applied Statistics, 38(5): 961–976.
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