Revert "Restructure docs.pymc.io, add developer guide (#3311)"

This reverts commit ff1227b.
pymc-devs · Dec 19, 2018 · 0d44707 · 0d44707
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Showing 11 changed files with 100 additions and 1,200 deletions.
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -147,7 +147,6 @@
         ("Examples", "nb_examples/index"),
         ("Books + Videos", "learn"),
         ("API", "api"),
-        ("Developer Guide", "developer_guide"),
         ("About PyMC3", "history")
     ],
     #     "fixed_sidebar": "false",

diff --git a/docs/source/developer_guide.rst b/docs/source/developer_guide.rst
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -10,10 +10,10 @@
                     <h3 class="ui header">Friendly modelling API</h3>
                     <p>PyMC3 allows you to write down models using an intuitive syntax to describe a data generating
                         process.</p>
-                    <h3 class="ui header">Cutting edge algorithms and model building blocks</h3>
+                    <h3 class="ui header">Cutting edge algorithms</h3>
                     <p>Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate
                         inference &mdash; including minibatch-ADVI for scaling to large datasets &mdash; or using
-                        Gaussian processes to build Bayesian nonparametric models.</p>
+                        Gaussian processes to fit a regression model.</p>
                 </div>
                 <div class="eight wide right floated column">
 

diff --git a/docs/source/notebooks/GLM.ipynb b/docs/source/notebooks/GLM.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# (Generalized) Linear and Hierarchical Linear Models in PyMC3"
+    "# GLM: Linear Regression"
    ]
   },
   {
@@ -28,7 +28,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Linear Regression\n",
+    "Simple example\n",
+    "==============\n",
     "\n",
     "Lets generate some data with known slope and intercept and fit a simple linear GLM."
    ]
@@ -145,7 +146,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Robust GLM\n",
+    "Robust GLM\n",
+    "==========\n",
     "\n",
     "Lets try the same model but with a few outliers in the data."
    ]
@@ -253,7 +255,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Hierarchical GLM"
+    "# Hierarchical GLM"
    ]
   },
   {
@@ -450,7 +452,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Logistic Regression"
+    "# Logistic Regression"
    ]
   },
   {

diff --git a/docs/source/notebooks/MvGaussianRandomWalk_demo.ipynb b/docs/source/notebooks/MvGaussianRandomWalk_demo.ipynb
diff --git a/docs/source/notebooks/api_quickstart.ipynb b/docs/source/notebooks/api_quickstart.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# General API quickstart"
+    "# API quickstart"
    ]
   },
   {

diff --git a/docs/source/notebooks/cox_model.ipynb b/docs/source/notebooks/cox_model.ipynb
@@ -1,12 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cox model"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -291,7 +284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.1"
   },
   "latex_envs": {
    "bibliofile": "biblio.bib",

diff --git a/docs/source/notebooks/gaussian_process.ipynb b/docs/source/notebooks/gaussian_process.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Gaussian Processes using numpy kernel\n",
+    "# Gaussian Processes\n",
     "\n",
     "(c) 2016 by Chris Fonnesbeck"
    ]

diff --git a/docs/source/notebooks/table_of_contents_examples.js b/docs/source/notebooks/table_of_contents_examples.js
@@ -1,27 +1,21 @@
 Gallery.contents = {
-    "BEST": "Case Studies",
-    "LKJ": "Case Studies",
-    "dawid-skene": "Case Studies",
-    "stochastic_volatility": "Case Studies",
-    "rugby_analytics": "Case Studies",
-    "multilevel_modeling": "Case Studies",
-    "Diagnosing_biased_Inference_with_Divergences": "Diagnostics and Model Criticism",
-    "model_comparison": "Diagnostics and Model Criticism",
-    "posterior_predictive": "Diagnostics and Model Criticism",
-    "Bayes_factor": "Diagnostics and Model Criticism",
-    "GLM": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-linear": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-logistic": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-hierarchical-binominal-model": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-hierarchical": "(Generalized) Linear and Hierarchical Linear Models",
-    "hierarchical_partial_pooling": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-model-selection": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-negative-binomial-regression": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-poisson-regression": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-robust-with-outlier-detection": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-robust": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-rolling-regression": "(Generalized) Linear and Hierarchical Linear Models",
-    "GLM-hierarchical-advi-minibatch": "(Generalized) Linear and Hierarchical Linear Models",
+    "AR": "Time Series",
+    "BEST": "Applied",
+    "Bayes_factor": "Other",
+    "Diagnosing_biased_Inference_with_Divergences": "Diagnostics",
+    "Euler-Maruyama_and_SDEs": "Time Series",
+    "GLM-hierarchical-advi-minibatch": "Variational Inference",
+    "GLM-hierarchical-binominal-model": "GLMs",
+    "GLM-hierarchical": "GLMs",
+    "GLM-linear": "GLMs",
+    "GLM-logistic": "GLMs",
+    "GLM-model-selection": "GLMs",
+    "GLM-negative-binomial-regression": "GLMs",
+    "GLM-poisson-regression": "GLMs",
+    "GLM-robust-with-outlier-detection": "GLMs",
+    "GLM-robust": "GLMs",
+    "GLM-rolling-regression": "GLMs",
+    "GLM": "GLMs",
     "GP-Kron": "Gaussian Processes",
     "GP-Latent": "Gaussian Processes",
     "GP-Marginal": "Gaussian Processes",
@@ -32,29 +26,32 @@ Gallery.contents = {
     "GP-TProcess": "Gaussian Processes",
     "GP-slice-sampling": "Gaussian Processes",
     "GP-smoothing": "Gaussian Processes",
-    "gaussian_process": "Gaussian Processes",
+    "MvGaussianRandomWalk_demo": "Time Series",
+    "SMC2_gaussians": "Other",
+    "bayes_param_survival_pymc3": "Survival Analysis",
+    "bayesian_neural_network_advi": "Variational Inference",
+    "bayesian_neural_network_with_sgfs": "Stochastic Gradients",
+    "censored_data": "Survival Analysis",
+    "constant_stochastic_gradient": "Stochastic Gradients",
+    "convolutional_vae_keras_advi": "Variational Inference",
+    "cox_model": "Other",
+    "dawid-skene": "Applied",
     "dependent_density_regression": "Mixture Models",
     "dp_mix": "Mixture Models",
+    "empirical-approx-overview": "Variational Inference",
     "gaussian-mixture-model-advi": "Mixture Models",
     "gaussian_mixture_model": "Mixture Models",
+    "gaussian_process": "Gaussian Processes",
+    "hierarchical_partial_pooling": "GLMs",
+    "lda-advi-aevb": "Variational Inference",
     "marginalized_gaussian_mixture_model": "Mixture Models",
-    "SMC2_gaussians": "Simulation-based Inference",
-    "bayesian_neural_network_with_sgfs": "Stochastic Gradients",
-    "constant_stochastic_gradient": "Stochastic Gradients",
+    "model_comparison": "Diagnostics",
+    "multilevel_modeling": "Applied",
+    "normalizing_flows_overview": "Variational Inference",
+    "posterior_predictive": "Diagnostics",
+    "rugby_analytics": "Applied",
     "sgfs_simple_optimization": "Stochastic Gradients",
-    "bayes_param_survival_pymc3": "Survival Analysis",
-    "censored_data": "Survival Analysis",
+    "stochastic_volatility": "Applied",
     "survival_analysis": "Survival Analysis",
-    "weibull_aft": "Survival Analysis",
-    "cox_model": "Survival Analysis",
-    "MvGaussianRandomWalk_demo": "Time Series",
-    "AR": "Time Series",
-    "Euler-Maruyama_and_SDEs": "Time Series",
-    "bayesian_neural_network_advi": "Variational Inference",
-    "convolutional_vae_keras_advi": "Variational Inference",
-    "empirical-approx-overview": "Variational Inference",
-    "lda-advi-aevb": "Variational Inference",
-    "normalizing_flows_overview": "Variational Inference",
-    "gaussian-mixture-model-advi": "Variational Inference",
-    "GLM-hierarchical-advi-minibatch": "Variational Inference"
+    "weibull_aft": "Survival Analysis"
 }
diff --git a/docs/source/notebooks/table_of_contents_tutorials.js b/docs/source/notebooks/table_of_contents_tutorials.js
@@ -1,20 +1,16 @@
 Gallery.contents = {
     "api_quickstart": "Basics",
-    "variational_api_quickstart": "Basics",
-    "theano": "Basics",
-    "prob_dists": "Basics",
-    "gp": "Basics",
-    "sampling_compound_step": "Deep dives",
-    "sampler-stats": "Deep dives",
-    "Diagnosing_biased_Inference_with_Divergences": "Deep dives",
-    "advanced_theano": "Deep dives",
-    "getting_started": "Deep dives",
-    "PyMC3_tips_and_heuristic": "How-To",
-    "blackbox_external_likelihood": "How-To",
+    "getting_started": "Basics",
+    "sampler-stats": "Basics",
+    "sampling_compound_step": "Basics",
+    "howto_debugging": "Basics",
+    "live_sample_plots": "How-To",
     "profiling": "How-To",
-    "howto_debugging": "How-To",
-    "model_averaging": "How-To",
     "updating_priors": "How-To",
-    "live_sample_plots": "How-To",
-    "lasso_block_update": "How-To"
+    "lasso_block_update": "How-To",
+    "model_averaging": "How-To",
+    "blackbox_external_likelihood": "How-To",
+    "LKJ": "How-To",
+    "variational_api_quickstart": "How-To",
+    "PyMC3_tips_and_heuristic": "How-To"
 }
diff --git a/docs/source/prob_dists.rst b/docs/source/prob_dists.rst
@@ -1,8 +1,8 @@
 .. _prob_dists:
 
-**********************************
-Probability Distributions in PyMC3
-**********************************
+*************************
+Probability Distributions
+*************************
 
 The most fundamental step in building Bayesian models is the specification of a full probability model for the problem at hand. This primarily involves assigning parametric statistical distributions to unknown quantities in the model, in addition to appropriate functional forms for likelihoods to represent the information from the data. To this end, PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks.