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michaelosthege
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Update NBs prior to 3.9.0 release (#3955)
* Re-run and fix many notebooks... Co-authored-by: Michael Osthege <[email protected]>
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74 files changed

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‎docs/source/notebooks/AR.ipynb

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‎docs/source/notebooks/BEST.ipynb

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‎docs/source/notebooks/Bayes_factor.ipynb

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‎docs/source/notebooks/DEMetropolisZ_EfficiencyComparison.ipynb

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‎docs/source/notebooks/DEMetropolisZ_tune_drop_fraction.ipynb

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‎docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb

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‎docs/source/notebooks/Euler-Maruyama_and_SDEs.ipynb

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],
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"source": [
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"%pylab inline\n",
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"import arviz as az\n",
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"import pymc3 as pm\n",
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"import theano.tensor as tt \n",
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"import scipy\n",
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"import theano.tensor as tt\n",
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"\n",
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"from pymc3.distributions.timeseries import EulerMaruyama"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%config InlineBackend.figure_format = 'retina'\n",
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"az.style.use('arviz-darkgrid')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"plot(zs, 'r', label='$z(t)$')\n",
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"legend()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pymc3 3.8\n",
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"arviz 0.8.3\n",
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"numpy 1.17.5\n",
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"last updated: Thu Jun 11 2020 \n",
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"\n",
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"CPython 3.8.2\n",
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"IPython 7.11.0\n",
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"watermark 2.0.2\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w"
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]
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}
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],
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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"version": "3.8.2"
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},
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"latex_envs": {
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"bibliofile": "biblio.bib",
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}

‎docs/source/notebooks/GLM-hierarchical-advi-minibatch.ipynb

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‎docs/source/notebooks/GLM-hierarchical-binominal-model.ipynb

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‎docs/source/notebooks/GLM-hierarchical.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import arviz as az\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pymc3 as pm \n",
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"import pandas as pd\n",
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"import pymc3 as pm \n",
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"import theano\n",
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"\n",
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"print('Running on PyMC3 v{}'.format(pm.__version__))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%config InlineBackend.figure_format = 'retina'\n",
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"az.style.use('arviz-darkgrid')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv(pm.get_data('radon.csv'))\n",
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"data['log_radon'] = data['log_radon'].astype(theano.config.floatX)\n",
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"county_names = data.county.unique()\n",
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"### Acknowledgements\n",
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"Thanks to [Imri Sofer](http://serre-lab.clps.brown.edu/person/imri-sofer/) for feedback and teaching us about the connections to random-effects models and [Dan Dillon](http://cdasr.mclean.harvard.edu/index.php/about-us/current-lab-members/14-faculty/62-daniel-dillon) for useful comments on an earlier draft."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pymc3 3.8\n",
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"arviz 0.8.3\n",
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"numpy 1.17.5\n",
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"last updated: Thu Jun 11 2020 \n",
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"\n",
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"CPython 3.8.2\n",
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"IPython 7.11.0\n",
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"watermark 2.0.2\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w"
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]
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}
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],
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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"version": "3.8.2"
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},
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"latex_envs": {
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"bibliofile": "biblio.bib",
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

‎docs/source/notebooks/GLM-linear.ipynb

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‎docs/source/notebooks/GLM-logistic.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import pandas as pd\n",
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"import arviz as az\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pymc3 as pm\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn\n",
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"import theano as thno\n",
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"import theano.tensor as T\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"from collections import OrderedDict\n",
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"from time import time\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from collections import OrderedDict\n",
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"from scipy.optimize import fmin_powell\n",
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"from scipy import integrate\n",
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"from time import time\n",
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"\n",
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"import theano as thno\n",
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"import theano.tensor as T"
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"print('Running on PyMC3 v{}'.format(pm.__version__))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%config InlineBackend.figure_format = 'retina'\n",
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"warnings.filterwarnings('ignore')\n",
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"az.style.use('arviz-darkgrid')"
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]
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},
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{
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": false
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},
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"metadata": {},
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"outputs": [
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{
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"data": {
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"## Model selection \n",
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"\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"WAIC confirms our decision to use age^2. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pymc3 3.8\n",
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"arviz 0.8.3\n",
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"numpy 1.17.5\n",
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"last updated: Thu Jun 11 2020 \n",
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"\n",
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"CPython 3.8.2\n",
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"IPython 7.11.0\n",
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"watermark 2.0.2\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w"
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]
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}
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],
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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"version": "3.8.2"
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},
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"latex_envs": {
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"bibliofile": "biblio.bib",
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}

‎docs/source/notebooks/GLM-model-selection.ipynb

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}
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],
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"source": [
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"%matplotlib inline\n",
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"import pymc3 as pm\n",
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"import arviz as az\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import pymc3 as pm\n",
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"import seaborn as sns\n",
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"\n",
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"from collections import OrderedDict\n",
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"from ipywidgets import interactive, fixed\n",
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"\n",
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"plt.style.use('seaborn-darkgrid')\n",
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"print('Running on PyMC3 v{}'.format(pm.__version__))\n",
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"print('Running on PyMC3 v{}'.format(pm.__version__))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%config InlineBackend.figure_format = 'retina'\n",
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"az.style.use('arviz-darkgrid')\n",
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"rndst = np.random.RandomState(0)"
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]
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},
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"source": [
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"Example originally contributed by Jonathan Sedar 2016-01-09 [github.com/jonsedar](https://github.com/jonsedar). Edited by Junpeng Lao 2017-07-6 [github.com/junpenglao](https://github.com/junpenglao)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"pymc3 3.8\n",
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"arviz 0.8.3\n",
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"numpy 1.17.5\n",
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"last updated: Thu Jun 11 2020 \n",
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"\n",
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"CPython 3.8.2\n",
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"IPython 7.11.0\n",
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"watermark 2.0.2\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w"
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]
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}
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],
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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"version": "3.8.2"
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},
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"widgets": {
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"state": {
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

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