Libary for SGCP (Sigmoidal Gaussian Cox Process) inference. This code accompanies the publication in Journal of Machine Learning Research.
Donner, C., & Opper, M. (2018). Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes. Journal of Machine Learning Research, 19(67).
Link to publication: http://www.jmlr.org/papers/v19/17-759.html
Minimal examples are given in example1D.py and example2D.py. The code was written with Python 3.6.
The Code was only developed for 1D and 2D examples. There is no hyperparameter optimisation implemented for the Laplace approximation.
For questions contact christian.donner(at)bccn-berlin.de
Copyright (C) 2018, Christian Donner
This file is part of SGCP_Inference.
SGCP_Inference is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
SGCP_Inference is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with SGCP_Inference. If not, see http://www.gnu.org/licenses/.