Fixing some minor typos in the docs

docs/source/advanced_theano.rst

@@ -7,7 +7,7 @@ Using shared variables
 
 Shared variables allow us to use values in theano functions that are
 not considered an input to the function, but can still be changed
-later. They are very similar to global variables in may ways.::
+later. They are very similar to global variables in may ways::
 
     a = tt.scalar('a')
     # Create a new shared variable with initial value of 0.1
@@ -23,7 +23,7 @@ their shape as long as the number of dimensions stays the same.
 
 We can use shared variables in PyMC3 to fit the same model to several
 datasets without the need to recreate the model each time (which can
-be time consuming if the number of datasets is large).::
+be time consuming if the number of datasets is large)::
 
     # We generate 10 datasets
     true_mu = [np.random.randn() for _ in range(10)]

docs/source/gp.rst

@@ -244,7 +244,7 @@ also need to include the additional arguments, :code:`X`, :code:`y`, and
 This second block produces the conditional distributions.  Notice that extra 
 arguments are required for conditionals of :math:`f1` and :math:`f2`, but not
 :math:`f`.  This is because those arguments are cached when calling 
-:code:`.marginal_likelihood` was called on :code:`gp`.
+:code:`.marginal_likelihood` on :code:`gp`.
 
 .. note::
   When constructing conditionals, the additional arguments :code:`X`, :code:`y`,

docs/source/intro.rst

@@ -69,7 +69,7 @@ PyMC began development in 2003, as an effort to generalize the process of
 building Metropolis-Hastings samplers, with an aim to making Markov chain Monte
 Carlo (MCMC) more accessible to applied scientists.
 The choice to develop PyMC as a python module, rather than a standalone
-application, allowed the use MCMC methods in a larger modeling framework. By
+application, allowed the use of MCMC methods in a larger modeling framework. By
 2005, PyMC was reliable enough for version 1.0 to be released to the public. A
 small group of regular users, most associated with the University of Georgia,
 provided much of the feedback necessary for the refinement of PyMC to a usable

docs/source/prob_dists.rst

@@ -22,7 +22,7 @@ A variable requires at least a ``name`` argument, and zero or more model paramet
 
         p = pm.Beta('p', 1, 1, shape=(3, 3))
 
-Probability distributions are all subclasses of ``Distribution``, which in turn has two major subclasses: ``Discrete`` and ``Continuous``. In terms of data types, a ``Continuous`` random variable is given whichever floating point type defined by ``theano.config.floatX``, while ``Discrete`` variables are given ``int16`` types when ``theano.config.floatX`` is ``float32``, and ``int64`` otherwise.
+Probability distributions are all subclasses of ``Distribution``, which in turn has two major subclasses: ``Discrete`` and ``Continuous``. In terms of data types, a ``Continuous`` random variable is given whichever floating point type is defined by ``theano.config.floatX``, while ``Discrete`` variables are given ``int16`` types when ``theano.config.floatX`` is ``float32``, and ``int64`` otherwise.
 
 All distributions in ``pm.distributions`` will have two important methods: ``random()`` and ``logp()`` with the following signatures:
 
@@ -128,4 +128,4 @@ The original variable is simply treated as a deterministic variable, since the v
     >>> model.deterministics
     [g]
 
-By default, auto-transformed variables are ignored when summarizing and plotting model output.
+By default, auto-transformed variables are ignored when summarizing and plotting model output.

docs/source/theano.rst

@@ -38,7 +38,7 @@ is similar to eg SymPy's `Symbol`)::
     y = tt.ivector('y')
 
 Next, we use those variables to build up a symbolic representation
-of the output of our function. Note, that no computation is actually
+of the output of our function. Note that no computation is actually
 being done at this point. We only record what operations we need to
 do to compute the output::
 
@@ -57,7 +57,7 @@ do to compute the output::
    we are working with symbolic input instead of plain arrays.
 
 Now we can tell Theano to build a function that does this computation.
-With a typical configuration Theano generates C code, compiles it,
+With a typical configuration, Theano generates C code, compiles it,
 and creates a python function which wraps the C function::
 
     func = theano.function([a, x, y], [out])
@@ -79,7 +79,7 @@ in `theano.sparse`. For a detailed overview of available operations,
 see `the theano api docs <http://deeplearning.net/software/theano/library/tensor/index.html>`_.
 
 A notable exception where theano variables do *not* behave like
-NumPy arrays are operations involving conditional execution:
+NumPy arrays are operations involving conditional execution.
 
 Code like this won't work as expected::