diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 4bcf1957..5ca86d9e 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -473,16 +473,16 @@ class GP(Model):
             self.inference_method.on_optimization_end()
             raise
 
-    def infer_newX(self, Y_new, optimize=True, ):
+    def infer_newX(self, Y_new, optimize=True):
         """
-        Infer the distribution of X for the new observed data *Y_new*.
+        Infer X for the new observed data *Y_new*.
 
         :param Y_new: the new observed data for inference
         :type Y_new: numpy.ndarray
         :param optimize: whether to optimize the location of new X (True by default)
         :type optimize: boolean
         :return: a tuple containing the posterior estimation of X and the model that optimize X
-        :rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` or numpy.ndarray, :class:`~GPy.core.model.Model`)
+        :rtype: (:class:`~GPy.core.parameterization.variational.VariationalPosterior` and numpy.ndarray, :class:`~GPy.core.model.Model`)
         """
         from ..inference.latent_function_inference.inferenceX import infer_newX
         return infer_newX(self, Y_new, optimize=optimize)
diff --git a/GPy/inference/latent_function_inference/inferenceX.py b/GPy/inference/latent_function_inference/inferenceX.py
index a8ed2d09..19013b06 100644
--- a/GPy/inference/latent_function_inference/inferenceX.py
+++ b/GPy/inference/latent_function_inference/inferenceX.py
@@ -27,12 +27,19 @@ def infer_newX(model, Y_new, optimize=True, init='L2'):
 
 class InferenceX(Model):
     """
-    The class for inference of new X with given new Y. (do_test_latent)
+    The model class for inference of new X with given new Y. (replacing the "do_test_latent" in Bayesian GPLVM)
+    It is a tiny inference model created from the original GP model. The kernel, likelihood (only Gaussian is supported at the moment) 
+    and posterior distribution are taken from the original model.
+    For Regression models and GPLVM, a point estimate of the latent variable X will be inferred. 
+    For Bayesian GPLVM, the variational posterior of X will be inferred. 
+    X is inferred through a gradient optimization of the inference model.
 
     :param model: the GPy model used in inference
     :type model: GPy.core.Model
     :param Y: the new observed data for inference
     :type Y: numpy.ndarray
+    :param init: the distance metric of Y for initializing X with the nearest neighbour.
+    :type init: 'L2', 'NCC' and 'rand'
     """
     def __init__(self, model, Y, name='inferenceX', init='L2'):
         if np.isnan(Y).any() or getattr(model, 'missing_data', False):