Merge branch 'master' into binomial_random

Author: fonnesbeck

.travis.yml

@@ -12,12 +12,12 @@ install:
   - pip install coveralls pylint
 
 env:
-  - PYTHON_VERSION=2.7 TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py"
+  - PYTHON_VERSION=2.7 TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py"
   - PYTHON_VERSION=2.7 RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py"
-  - PYTHON_VERSION=2.7 TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_variational_inference.py"
-  - PYTHON_VERSION=3.6 TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py"
+  - PYTHON_VERSION=2.7 TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
+  - PYTHON_VERSION=3.6 TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py"
   - PYTHON_VERSION=3.6 TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py"
-  - PYTHON_VERSION=3.6 TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_variational_inference.py"
+  - PYTHON_VERSION=3.6 TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
 script:
   - . ./scripts/test.sh $TESTCMD
 

README.rst

@@ -34,21 +34,22 @@ Features
 Getting started
 ---------------
 
+If you already know about Bayesian statistics:
+
+-  `API quickstart guide <http://pymc-devs.github.io/pymc3/notebooks/api_quickstart.html>`__
 -  The `PyMC3 tutorial <http://pymc-devs.github.io/pymc3/notebooks/getting_started.html>`__
 -  `PyMC3 examples <http://pymc-devs.github.io/pymc3/examples.html>`__
    and the `API reference <http://pymc-devs.github.io/pymc3/api.html>`__
--  `Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>`__
--  `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>`__
--  `PyMC3 port of the models presented in the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers <https://github.com/junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3>`__
--  `Coyle P. (2016) Probabilistic programming and PyMC3. European Scientific Python Conference 2015 (Cambridge, UK) <http://adsabs.harvard.edu/abs/2016arXiv160700379C>`__
--  `Bayesian Analysis with Python by Osvaldo Martin <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python>`__ (and `errata <https://github.com/aloctavodia/BAP>`__)
+
+If you want to learn Bayesian statistics together with PyMC3:
+
+-  `Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>`__: Fantastic book with many applied code examples.
+-  `PyMC3 port of the models presented in the book "Doing Bayesian Data Analysis" by John Kruschke <https://github.com/aloctavodia/Doing_bayesian_data_analysis>`__: Principled introduction to Bayesian data analysis.
+-  `PyMC3 port of the examples in "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath <https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3>`__
+-  `PyMC3 port of the models presented in the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers <https://github.com/junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3>`__: Focused on using Bayesian statistics in cognitive modeling.
+-  `Bayesian Analysis with Python by Osvaldo Martin <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python>`__ (and `errata <https://github.com/aloctavodia/BAP>`__): Great introduction.
+
+There are also several talks on PyMC3 which are gathered in this `YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>`__
 
 Installation
 ------------

RELEASE-NOTES.md

@@ -6,6 +6,8 @@
 
 * [Add Gaussian Process submodule](http://pymc-devs.github.io/pymc3/notebooks/GP-introduction.html)
 
+* sample() defaults changed: tuning is enabled for the first 500 samples which are then discarded from the trace as burn-in.
+
 * Many optimizations and speed-ups.
 
 * NUTS implementation now matches current Stan implementation.

docs/source/examples.rst

@@ -8,6 +8,7 @@ Howto
 =====
 
 .. toctree::
+   notebooks/api_quickstart.ipynb
    notebooks/sampler-stats.ipynb
    notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb
    notebooks/posterior_predictive.ipynb

docs/source/getting_started.rst

@@ -6,3 +6,4 @@ Getting started
 
 .. toctree::
    notebooks/getting_started.ipynb
+   notebooks/api_quickstart.ipynb

docs/source/notebooks/GLM-robust-with-outlier-detection.ipynb

@@ -665,6 +665,30 @@
     "            lines={k: v['mean'] for k, v in pm.df_summary(traces_signoise[-1000:]).iterrows()})"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "def f(x, y):\n",
+    "    print(x)\n",
+    "    print(y)\n",
+    "    \n",
+    "f(y=1, x=2)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},