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DOC Add missing examples, fix some minor headings and made file names more regular.
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‎docs/source/examples.rst

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notebooks/GLM-robust.ipynb
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notebooks/GLM-robust-with-outlier-detection.ipynb
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notebooks/GLM-model-selection.ipynb
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notebooks/rolling_regression.ipynb
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notebooks/GLM-rolling-regression.ipynb
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notebooks/GLM-logistic.ipynb
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notebooks/GLM-hierarchical.ipynb
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notebooks/GLM-hierarchical-ADVI.ipynb
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notebooks/GLM-hierarchical-advi-minibatch.ipynb
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notebooks/GLM-poisson-regression.ipynb
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notebooks/pmf-pymc.ipynb
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notebooks/rugby_analytics.ipynb
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notebooks/posterior_predictive.ipynb
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notebooks/Bayesian_LogReg.ipynb
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notebooks/dp_mix.ipynb
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notebooks/bayesian_neural_network_advi.ipynb
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notebooks/lda-advi-aevb.ipynb
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notebooks/gaussian_mixture_model.ipynb
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notebooks/gaussian-mixture-model-advi.ipynb
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notebooks/howto_debugging.ipynb

‎docs/source/notebooks/GLM-hierarchical-ADVI.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# The best of both worlds: Hierarchical Linear Regression in PyMC3"
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"# GLM: Hierarchical Linear Regression with ADVI"
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}
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"metadata": {
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"anaconda-cloud": {},
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"display_name": "Python [default]",
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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": "3.5.2"
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‎docs/source/notebooks/GLM-hierarchical-advi-minibatch.ipynb

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"cell_type": "markdown",
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"source": [
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"# Mini-batch ADVI on hierarchical regression model\n",
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"# GLM: Mini-batch ADVI on hierarchical regression model\n",
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"Unlike Gaussian mixture models, (hierarchical) regression models have independent variables. These variables affect the likelihood function, but are not random variables. When using mini-batch, we should take care of that. "
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}
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"metadata": {
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"anaconda-cloud": {},
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"name": "python3"
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‎docs/source/notebooks/GLM-hierarchical.ipynb

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"cell_type": "markdown",
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"source": [
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"# Hierarchical Linear Regression"
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"# GLM: Hierarchical Linear Regression"
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}
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‎docs/source/notebooks/GLM-linear.ipynb

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"name": "python",
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‎docs/source/notebooks/GLM-logistic.ipynb

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

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"# GLM Model Selection\n",
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"# GLM: Model Selection\n",
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"\n",
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"**A fairly minimal reproducable example of Model Selection using DIC and WAIC.**\n",
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‎docs/source/notebooks/GLM-poisson-regression.ipynb

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"##### PyMC3 Examples\n",
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"# GLM: Poisson Regression\n",
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"\n",
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"# GLM Poisson Regression\n",
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"\n",
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"#### A minimal reproducable example of poisson regression to predict counts using dummy data.\n",
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"## A minimal reproducable example of poisson regression to predict counts using dummy data.\n",
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"This Notebook is basically an excuse to demo poisson regression using PyMC3, both manually and using the `glm` library to demo interactions using the `patsy` library. We will create some dummy data, poisson distributed according to a linear model, and try to recover the coefficients of that linear model through inference.\n",
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"# Setup"
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"## Setup"
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"# Poisson Regression"
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"## Poisson Regression"
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"## 1. Manual method, create design matrices and manually specify model"
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"### 1. Manual method, create design matrices and manually specify model"
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"### Create Model"
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"#### Create Model"
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"### Sample Model"
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"#### Sample Model"
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"### View Diagnostics"
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"#### View Diagnostics"
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"## Transform coeffs and recover theta values"
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"### Transform coeffs and recover theta values"
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"## 2. Alternative method, using `pymc.glm`"
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"### 2. Alternative method, using `pymc.glm`"
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"### Create Model"
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"#### Create Model"
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"### Sample Model"
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"#### Sample Model"
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"### View Traces"
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"#### View Traces"
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‎docs/source/notebooks/GLM-robust-with-outlier-detection.ipynb

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"# GLM Robust Regression with Outlier Detection\n",
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"# GLM: Robust Regression with Outlier Detection\n",
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"**A minimal reproducable example of Robust Regression with Outlier Detection using Hogg 2010 Signal vs Noise method.**\n",
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‎docs/source/notebooks/GLM-robust.ipynb

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"source": [
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"# Table of Contents\n",
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" <p><div class=\"lev1\"><a href=\"#GLM:-Robust-Linear-Regression\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>GLM: Robust Linear Regression</a></div><div class=\"lev2\"><a href=\"#Robust-Regression\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Robust Regression</a></div><div class=\"lev2\"><a href=\"#Summary\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Summary</a></div>"
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"# GLM: Robust Linear Regression\n",
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"Author: Thomas Wiecki\n",
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"Author: [Thomas Wiecki](https://twitter.com/twiecki)\n",
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"This tutorial first appeard as a post in small series on Bayesian GLMs on my blog:\n",
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