diff --git a/src/shogun/machine/gp/LaplacianInferenceMethodWithLBFGS.h b/src/shogun/machine/gp/LaplacianInferenceMethodWithLBFGS.h
deleted file mode 100644
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--- a/src/shogun/machine/gp/LaplacianInferenceMethodWithLBFGS.h
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-/*
- * This program 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.
- *
- * Written (W) 2014 Wu Lin
- * Copyright (C) 2012 Jacob Walker
- * Copyright (C) 2013 Roman Votyakov
- * Copyright (C) 2014 Wu Lin
- *
- * Code adapted from Gaussian Process Machine Learning Toolbox
- * http://www.gaussianprocess.org/gpml/code/matlab/doc/
- */
-
-#ifndef CLAPLACIANINFERENCEMETHODWITHLBFGS_H_
-#define CLAPLACIANINFERENCEMETHODWITHLBFGS_H_
-
-#include <shogun/lib/config.h>
-
-#ifdef HAVE_EIGEN3
-
-#include <shogun/machine/gp/LaplacianInferenceMethod.h>
-#include <shogun/mathematics/eigen3.h>
-
-
-namespace shogun {
-
-/** @brief The Laplace approximation inference method with LBFGS class.
- *
- * This inference method approximates the posterior likelihood function by using
- * Laplace's method. Here, we compute a Gaussian approximation to the posterior
- * via a Taylor expansion around the maximum of the posterior likelihood
- * function.
- *
- * For more details, see "Bayesian Classification with Gaussian Processes" by
- * Christopher K.I Williams and David Barber, published 1998 in the IEEE
- * Transactions on Pattern Analysis and Machine Intelligence, Volume 20, Number
- * 12, Pages 1342-1351.
- *
- * This specific implementation was adapted from the infLaplace.m file in the
- * GPML toolbox.
- */
-
-/** Wrapper class used for the LBFGS minimizer */
-class CSharedInfoForLBFGS {
-public:
- index_t dim;
- Eigen::Map<Eigen::MatrixXd>* kernel;
- Eigen::Map<Eigen::VectorXd>* mean_f;
- CSharedInfoForLBFGS()
-     :dim(0), kernel(NULL), mean_f(NULL){}
-};
-
-
-class CLaplacianInferenceMethodWithLBFGS: public CLaplacianInferenceMethod {
-public:
- /** default constructor */
- CLaplacianInferenceMethodWithLBFGS();
-
- /** constructor
-  *
-  * @param kernel covariance function
-  * @param features features to use in inference
-  * @param mean mean function
-  * @param labels labels of the features
-  * @param model Likelihood model to use
-  */
- CLaplacianInferenceMethodWithLBFGS(CKernel* kernel, 
-                                   CFeatures* features,
-                                   CMeanFunction* mean, 
-                                   CLabels* labels, 
-                                   CLikelihoodModel* model);
-
- virtual ~CLaplacianInferenceMethodWithLBFGS();
-
- /** returns the name of the inference method
-  *
-  * @return name LaplacianWithLBFGS
-  */
- virtual const char* get_name() const { return "LaplacianInferenceMethodWithLBFGS"; }
-
-protected:
- /** update alpha using the LBFGS method*/
- virtual void update_alpha();
-
- /** compute the gradient given the current alpha*/
- virtual void get_gradient_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha,
-                                     Eigen::Map<Eigen::VectorXd>* gradient);
-
- /** compute the function value given the current alpha*/
- virtual void get_psi_wrt_alpha(Eigen::Map<Eigen::VectorXd>* alpha, 
-                                float64_t* psi);
-
- /** only uses for dimension check*/
- void check(int left, int right, int line);
-
- /** variables needed to compute gradient and function value*/
- CSharedInfoForLBFGS m_shared;
-};
-
-}
-#endif /* HAVE_EIGEN3 */
-#endif /* CLAPLACIANINFERENCEMETHODWITHLBFGS_H_ */