DOC Fix link to getting started guide. Close #1256.

Author: twiecki

readme.md

@@ -23,7 +23,7 @@ PyMC3 is beta software. Users should consider using [PyMC 2 repository](https://
  * Transparent support for missing value imputation
 
 ## Getting started
- * The [PyMC3 tutorial](http://pymc-devs.github.io/pymc3/getting_started) or [journal publication](https://peerj.com/articles/cs-55/)
+ * The [PyMC3 tutorial](http://pymc-devs.github.io/pymc3/notebooks/getting_started.html) or [journal publication](https://peerj.com/articles/cs-55/)
  * [PyMC3 examples](http://pymc-devs.github.io/pymc3/examples.html) and the [API reference](http://pymc-devs.github.io/pymc3/api.html)
  * [Bayesian Modelling in Python -- tutorials on Bayesian statistics and PyMC3 as Jupyter Notebooks by Mark Dregan](https://github.com/markdregan/Bayesian-Modelling-in-Python)
  * [Talk at PyData London 2016 on PyMC3](https://www.youtube.com/watch?v=LlzVlqVzeD8)

readme.rst

@@ -0,0 +1,144 @@
+PyMC3
+=====
+
+|Gitter|
+
+|Build Status|
+
+PyMC3 is a python module for Bayesian statistical modeling and model
+fitting which focuses on advanced Markov chain Monte Carlo fitting
+algorithms. Its flexibility and extensibility make it applicable to a
+large suite of problems.
+
+Check out the `getting started
+guide <http://pymc-devs.github.io/pymc3/notebooks/getting_started.html>`__!
+
+PyMC3 is beta software. Users should consider using `PyMC 2
+repository <https://github.com/pymc-devs/pymc>`__.
+
+Features
+--------
+
+-  Intuitive model specification syntax, for example, ``x ~ N(0,1)``
+   translates to ``x = Normal(0,1)``
+-  **Powerful sampling algorithms**, such as the `No U-Turn
+   Sampler <http://arxiv.org/abs/1111.4246>`__, allow complex models
+   with thousands of parameters with little specialized knowledge of
+   fitting algorithms.
+-  **Variational inference**: `ADVI <http://arxiv.org/abs/1506.03431>`__
+   for fast approximate posterior estimation as well as mini-batch ADVI
+   for large data sets.
+-  Easy optimization for finding the *maximum a posteriori* (MAP) point
+-  `Theano <http://deeplearning.net/software/theano/>`__ features
+-  Numpy broadcasting and advanced indexing
+-  Linear algebra operators
+-  Computation optimization and dynamic C compilation
+-  Simple extensibility
+-  Transparent support for missing value imputation
+
+Getting started
+---------------
+
+-  The `PyMC3
+   tutorial <http://pymc-devs.github.io/pymc3/notebooks/getting_started.html>`__ or
+   `journal publication <https://peerj.com/articles/cs-55/>`__
+-  `PyMC3 examples <http://pymc-devs.github.io/pymc3/examples.html>`__
+   and the `API reference <http://pymc-devs.github.io/pymc3/api.html>`__
+-  `Bayesian Modelling in Python -- tutorials on Bayesian statistics and
+   PyMC3 as Jupyter Notebooks by Mark
+   Dregan <https://github.com/markdregan/Bayesian-Modelling-in-Python>`__
+-  `Talk at PyData London 2016 on
+   PyMC3 <https://www.youtube.com/watch?v=LlzVlqVzeD8>`__
+-  `PyMC3 port of the models presented in the book "Doing Bayesian Data
+   Analysis" by John
+   Kruschke <https://github.com/aloctavodia/Doing_bayesian_data_analysis>`__
+-  Coal Mining Disasters model in `PyMC
+   2 <https://github.com/pymc-devs/pymc/blob/master/pymc/examples/disaster_model.py>`__
+   and `PyMC
+   3 <https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/disaster_model.py>`__
+
+Installation
+------------
+
+The latest version of PyMC3 can be installed from the master branch
+using pip:
+
+::
+
+    pip install git+https://github.com/pymc-devs/pymc3
+
+To ensure the development branch of Theano is installed alongside PyMC3
+(recommended), you can install PyMC3 using the ``requirements.txt``
+file. This requires cloning the repository to your computer:
+
+::
+
+    git clone https://github.com/pymc-devs/pymc3
+    cd pymc3
+    pip install -r requirements.txt
+
+However, if a recent version of Theano has already been installed on
+your system, you can install PyMC3 directly from GitHub.
+
+Another option is to clone the repository and install PyMC3 using
+``python setup.py install`` or ``python setup.py develop``.
+
+**Note:** Running ``pip install pymc`` will install PyMC 2.3, not PyMC3,
+from PyPI.
+
+Dependencies
+------------
+
+PyMC3 is tested on Python 2.7 and 3.3 and depends on Theano, NumPy,
+SciPy, Pandas, and Matplotlib (see ``requirements.txt`` for version
+information).
+
+Optional
+~~~~~~~~
+
+In addtion to the above dependencies, the GLM submodule relies on
+``Patsy``\ [http://patsy.readthedocs.io/en/latest/].
+
+```scikits.sparse`` <https://github.com/njsmith/scikits-sparse>`__
+enables sparse scaling matrices which are useful for large problems.
+Installation on Ubuntu is easy:
+
+::
+
+    sudo apt-get install libsuitesparse-dev
+    pip install git+https://github.com/njsmith/scikits-sparse.git
+
+On Mac OS X you can install libsuitesparse 4.2.1 via homebrew (see
+http://brew.sh/ to install homebrew), manually add a link so the include
+files are where scikits-sparse expects them, and then install
+scikits-sparse:
+
+::
+
+    brew install suite-sparse
+    ln -s /usr/local/Cellar/suite-sparse/4.2.1/include/ /usr/local/include/suitesparse
+    pip install git+https://github.com/njsmith/scikits-sparse.git
+
+Citing PyMC3
+------------
+
+Salvatier J, Wiecki TV, Fonnesbeck C. (2016) Probabilistic programming
+in Python using PyMC3. PeerJ Computer Science 2:e55
+https://doi.org/10.7717/peerj-cs.55
+
+License
+-------
+
+`Apache License, Version
+2.0 <https://github.com/pymc-devs/pymc3/blob/master/LICENSE>`__
+
+Contributors
+------------
+
+See the `GitHub contributor
+page <https://github.com/pymc-devs/pymc3/graphs/contributors>`__
+
+.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg
+   :target: https://gitter.im/pymc-devs/pymc?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
+.. |Build Status| image:: https://travis-ci.org/pymc-devs/pymc3.png?branch=master
+   :target: https://travis-ci.org/pymc-devs/pymc3