diff --git a/examples/generalized_linear_models/GLM-out-of-sample-predictions.ipynb b/examples/generalized_linear_models/GLM-out-of-sample-predictions.ipynb
new file mode 100644
index 000000000..d02790046
--- /dev/null
+++ b/examples/generalized_linear_models/GLM-out-of-sample-predictions.ipynb
@@ -0,0 +1,655 @@
+{
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6-final"
+ },
+ "orig_nbformat": 2,
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3.7.6 64-bit ('website_projects': conda)",
+ "metadata": {
+ "interpreter": {
+ "hash": "fbddea5140024843998ae64bf59a7579a9660d103062604797ea5984366c686c"
+ }
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+ {
+ "source": [
+ "# GLM in PyMC3: Out-Of-Sample Predictions\n",
+ "\n",
+ "In this notebook I explore the [glm](https://docs.pymc.io/api/glm.html) module of [PyMC3](https://docs.pymc.io/). I am particularly interested in the model definition using [patsy](https://patsy.readthedocs.io/en/latest/) formulas, as it makes the model evaluation loop faster (easier to include features and/or interactions). There are many good resources on this subject, but most of them evaluate the model in-sample. For many applications we require doing predictions on out-of-sample data. This experiment was motivated by the discussion of the thread [\"Out of sample\" predictions with the GLM sub-module](https://discourse.pymc.io/t/out-of-sample-predictions-with-the-glm-sub-module/773) on the (great!) forum [discourse.pymc.io/](https://discourse.pymc.io/), thank you all for your input!\n",
+ "\n",
+ "**Resources**\n",
+ "\n",
+ "\n",
+ "- [PyMC3 Docs: Example Notebooks](https://docs.pymc.io/nb_examples/index.html)\n",
+ " \n",
+ " - In particular check [GLM: Logistic Regression](https://docs.pymc.io/notebooks/GLM-logistic.html)\n",
+ "\n",
+ "- [Bambi](https://bambinos.github.io/bambi/), a more complete implementation of the GLM submodule which also allows for mixed-effects models.\n",
+ "\n",
+ "- [Bayesian Analysis with Python (Second edition) - Chapter 4](https://github.com/aloctavodia/BAP/blob/master/code/Chp4/04_Generalizing_linear_models.ipynb)\n",
+ "- [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)"
+ ],
+ "cell_type": "markdown",
+ "metadata": {}
+ },
+ {
+ "source": [
+ "## Prepare Notebook"
+ ],
+ "cell_type": "markdown",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "sns.set_style(style=\"darkgrid\", rc={\"axes.facecolor\": \".9\", \"grid.color\": \".8\"})\n",
+ "sns.set_palette(palette=\"deep\")\n",
+ "sns_c = sns.color_palette(palette=\"deep\")\n",
+ "\n",
+ "import arviz as az\n",
+ "import patsy\n",
+ "import pymc3 as pm\n",
+ "\n",
+ "from pymc3 import glm\n",
+ "\n",
+ "plt.rcParams[\"figure.figsize\"] = [7, 6]\n",
+ "plt.rcParams[\"figure.dpi\"] = 100"
+ ]
+ },
+ {
+ "source": [
+ "## Generate Sample Data\n",
+ "\n",
+ "We want to fit a logistic regression model where there is a multiplicative interaction between two numerical features."
+ ],
+ "cell_type": "markdown",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " x1 x2 y\n",
+ "0 0.993428 -2.521768 0\n",
+ "1 -0.276529 1.835724 0\n",
+ "2 1.295377 4.244312 1\n",
+ "3 3.046060 2.064931 1\n",
+ "4 -0.468307 -3.038740 1"
+ ],
+ "text/html": "\n\n
\n \n \n | \n x1 | \n x2 | \n y | \n
\n \n \n \n | 0 | \n 0.993428 | \n -2.521768 | \n 0 | \n
\n \n | 1 | \n -0.276529 | \n 1.835724 | \n 0 | \n
\n \n | 2 | \n 1.295377 | \n 4.244312 | \n 1 | \n
\n \n | 3 | \n 3.046060 | \n 2.064931 | \n 1 | \n
\n \n | 4 | \n -0.468307 | \n -3.038740 | \n 1 | \n
\n \n
\n