diff --git a/src/shogun/CMakeLists.txt b/src/shogun/CMakeLists.txt
index 31a0d2c7b..e700bd7c7 100644
--- a/src/shogun/CMakeLists.txt
+++ b/src/shogun/CMakeLists.txt
@@ -307,7 +307,7 @@ IF(NOT EIGEN3_FOUND)
   )
 ELSE()
   # https://github.com/shogun-toolbox/shogun/issues/4870
-  IF(${EIGEN3_VERSION_STRING} VERSION_GREATER 3.3.9)
+  IF(0)
     MESSAGE(FATAL_ERROR "The system Eigen3 version ${EIGEN3_VERSION_STRING} isn't supported!")
   ENDIF()
   SHOGUN_INCLUDE_DIRS(SCOPE PUBLIC SYSTEM ${EIGEN3_INCLUDE_DIR})
diff --git a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
index a1677177e..0c9ca8f78 100644
--- a/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
+++ b/src/shogun/machine/gp/MultiLaplaceInferenceMethod.cpp
@@ -87,10 +87,10 @@ public:
 		float64_t result=0;
 		for(index_t bl=0; bl<C; bl++)
 		{
-			eigen_f.block(bl * n, 0, n, 1) =
-				K * alpha->block(bl * n, 0, n, 1) * std::exp(log_scale * 2.0);
-			result+=alpha->block(bl*n,0,n,1).dot(eigen_f.block(bl*n,0,n,1))/2.0;
-			eigen_f.block(bl*n,0,n,1)+=eigen_m;
+			eigen_f.segment(bl * n,  n) =
+				K * alpha->segment(bl * n,  n) * std::exp(log_scale * 2.0);
+			result+=alpha->segment(bl*n,n).dot(eigen_f.segment(bl*n,n))/2.0;
+			eigen_f.segment(bl*n,n)+=eigen_m;
 		}
 
 		// get first and second derivatives of log likelihood
@@ -278,9 +278,9 @@ void MultiLaplaceInferenceMethod::update_alpha()
 	{
 		Map<VectorXd> alpha(m_alpha.vector, m_alpha.vlen);
 		for(index_t bl=0; bl<C; bl++)
-			eigen_mu.block(bl * n, 0, n, 1) = eigen_ktrtr *
+			eigen_mu.segment(bl * n, n) = eigen_ktrtr *
 				                              std::exp(m_log_scale * 2.0) *
-				                              alpha.block(bl * n, 0, n, 1);
+				                              alpha.segment(bl * n, n);
 
 		//alpha'*(f-m)/2.0
 		Psi_New=alpha.dot(eigen_mu)/2.0;
@@ -324,7 +324,7 @@ void MultiLaplaceInferenceMethod::update_alpha()
 
 		for(index_t bl=0; bl<C; bl++)
 		{
-			VectorXd eigen_sD=eigen_dpi.block(bl*n,0,n,1).cwiseSqrt();
+			VectorXd eigen_sD=eigen_dpi.segment(bl*n,n).cwiseSqrt();
 			LLT<MatrixXd> chol_tmp(
 				(eigen_sD * eigen_sD.transpose())
 				    .cwiseProduct(eigen_ktrtr * std::exp(m_log_scale * 2.0)) +
@@ -351,14 +351,14 @@ void MultiLaplaceInferenceMethod::update_alpha()
 		VectorXd tmp2=m_tmp.array().rowwise().sum();
 
 		for(index_t bl=0; bl<C; bl++)
-			eigen_b.block(bl*n,0,n,1)+=eigen_dpi.block(bl*n,0,n,1).cwiseProduct(eigen_mu.block(bl*n,0,n,1)-eigen_mean_bl-tmp2);
+			eigen_b.segment(bl*n,n)+=eigen_dpi.segment(bl*n,n).cwiseProduct(eigen_mu.segment(bl*n,n)-eigen_mean_bl-tmp2);
 
 		Map<VectorXd> &eigen_c=eigen_W;
 		for(index_t bl=0; bl<C; bl++)
-			eigen_c.block(bl * n, 0, n, 1) =
+			eigen_c.segment(bl * n, n) =
 				eigen_E.block(0, bl * n, n, n) *
 				(eigen_ktrtr * std::exp(m_log_scale * 2.0) *
-				 eigen_b.block(bl * n, 0, n, 1));
+				 eigen_b.segment(bl * n, n));
 
 		Map<MatrixXd> c_tmp(eigen_c.data(),n,C);
 
@@ -422,7 +422,7 @@ float64_t MultiLaplaceInferenceMethod::get_derivative_helper(SGMatrix<float64_t>
 	{
 		result+=((eigen_E.block(0,bl*n,n,n)-eigen_U.block(0,bl*n,n,n).transpose()*eigen_U.block(0,bl*n,n,n)).array()
 			*eigen_dK.array()).sum();
-		result-=(eigen_dK*eigen_alpha.block(bl*n,0,n,1)).dot(eigen_alpha.block(bl*n,0,n,1));
+		result-=(eigen_dK*eigen_alpha.segment(bl*n,n)).dot(eigen_alpha.segment(bl*n,n));
 	}
 
 	return result/2.0;
@@ -504,7 +504,7 @@ SGVector<float64_t> MultiLaplaceInferenceMethod::get_derivative_wrt_mean(
 		result[i]=0;
 		//currently only compute the explicit term
 		for(index_t bl=0; bl<C; bl++)
-			result[i]-=eigen_alpha.block(bl*n,0,n,1).dot(eigen_dmu);
+			result[i]-=eigen_alpha.segment(bl*n,n).dot(eigen_dmu);
 	}
 
 	return result;