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PGM-Lab · Aug 3, 2018 · 7253006 · 7253006
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diff --git a/README.rst b/README.rst
@@ -7,14 +7,14 @@
    	:scale: 90 %
    	:align: center
 
-InferPy: Deep Probabilistic Modelling Made Easy
+InferPy: Deep Probabilistic Modeling Made Easy
 ===============================================
 
 
 InferPy is a high-level API for probabilistic modeling written in Python and 
 capable of running on top of Edward and Tensorflow. InferPy's API is 
 strongly inspired by Keras and it has a focus on enabling flexible data processing, 
-easy-to-code probablistic modelling, scalable inference and robust model validation. 
+easy-to-code probablistic modeling, scalable inference and robust model validation.
 
 Use InferPy is you need a probabilistic programming language that:
 

diff --git a/docs/index.rst b/docs/index.rst
@@ -3,7 +3,7 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-InferPy: Probabilistic Modelling with Tensorflow Made Easy
+InferPy: Probabilistic Modeling with Tensorflow Made Easy
 ==========================================================
 
 .. image:: _static/img/logo.png
@@ -15,7 +15,7 @@ InferPy: Probabilistic Modelling with Tensorflow Made Easy
 InferPy is a high-level API for probabilistic modeling written in Python and 
 capable of running on top of Tensorflow. InferPy's API is 
 strongly inspired by Keras and it has a focus on enabling flexible data processing, 
-easy-to-code probablistic modelling, scalable inference and robust model validation. 
+easy-to-code probablistic modeling, scalable inference and robust model validation.
 
 Use InferPy if you need a probabilistic programming language that:
 

diff --git a/docs/notes/guidebayesian.rst b/docs/notes/guidebayesian.rst
@@ -17,7 +17,7 @@ Bayesian deep learning or deep probabilistic programming enbraces the
 idea of employing deep neural networks within a probabilistic model in
 order to capture complex non-linear dependencies between variables.
 
-InferPy's API gives support to this powerful and flexible modelling
+InferPy's API gives support to this powerful and flexible modeling
 framework. Let us start by showing how a variational autoencoder over
 binary data can be defined by mixing Keras and InferPy code.
 

diff --git a/docs/notes/guidemodels.rst b/docs/notes/guidemodels.rst
@@ -517,7 +517,7 @@ where the input parameter ``loc`` and ``scale`` correspond to :math:`\mu` and :m
 Poisson
 ~~~~~~~~~~~~~~~
 
-The Poisson distribution is a discrete probability distribution for modelling the number of times an event occurs
+The Poisson distribution is a discrete probability distribution for modeling the number of times an event occurs
 in an interval of time or space. Its probability mass function is
 
 

diff --git a/setup.py b/setup.py
@@ -10,12 +10,12 @@
 setup(
     name='inferpy',
     version=__version__,
-    description='Probabilistic modelling with Tensorflow made easy',
+    description='Probabilistic modeling with Tensorflow made easy',
     author='Andrés R. Masegosa, Rafael Cabañas',
     author_email="[email protected], [email protected]",
     packages=['inferpy',
               'inferpy.models', 'inferpy.util', 'inferpy.criticism', 'inferpy.inferences' ],
-    install_requires=['numpy>=1.7', 'tensorflow>=1.2.0rc0'],
+    install_requires=['numpy>=1.7', 'tensorflow >=1.2.0rc0, <1.8'],
     extras_require={
         'tensorflow with gpu': ['tensorflow-gpu>=1.2.0rc0'],
         'visualization': ['matplotlib>=1.3',