Update README.md

README.md

@@ -15,7 +15,7 @@ The theory for the augmentation is given in the following paper : [Automated Aug
 
 # Packages models :
 
-## Two GP classification likelihood
+## Two GP classification likelihoods
   - **BayesianSVM** : A Classifier with a likelihood equivalent to the classic SVM [IJulia example](https://nbviewer.jupyter.org/github/theogf/AugmentedGaussianProcesses.jl/blob/master/examples/Classification%20-%20BayesianSVM.ipynb)/[Reference][arxivbsvm]
   - **Logistic** : A Classifier with a Bernoulli likelihood with the logistic link [IJulia example](https://nbviewer.jupyter.org/github/theogf/AugmentedGaussianProcesses.jl/blob/master/examples/Classification%20-%20Logistic.ipynb)/[Reference][arxivxgpc]
 
@@ -24,7 +24,7 @@ The theory for the augmentation is given in the following paper : [Automated Aug
 </p>
 
 ---
-## Four GP Regression likelihood
+## Four GP Regression likelihoods
   - **Gaussian** : The standard Gaussian Process regression model with a Gaussian Likelihood (no data augmentation was needed here) [IJulia example](https://nbviewer.jupyter.org/github/theogf/AugmentedGaussianProcesses.jl/blob/master/examples/Regression%20-%20Gaussian.ipynb)/[Reference][arxivgpbigdata]
   - **StudentT** : The standard Gaussian Process regression with a Student-t likelihood (the degree of freedom ν is not optimizable for the moment) [IJulia example](https://nbviewer.jupyter.org/github/theogf/AugmentedGaussianProcesses.jl/blob/master/examples/Regression%20-%20StudentT.ipynb)/[Reference][jmlrstudentt]
   - **Laplace** : Gaussian Process regression with a Laplace likelihood [IJulia example](https://nbviewer.jupyter.org/github/theogf/AugmentedGaussianProcesses.jl/blob/master/examples/Regression%20-%20Laplace.ipynb)/(No reference at the moment)