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Revert "Restructure docs.pymc.io, add developer guide (#3311)"
This reverts commit ff1227b.
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‎docs/source/conf.py

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("Examples", "nb_examples/index"),
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("Books + Videos", "learn"),
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("API", "api"),
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("Developer Guide", "developer_guide"),
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("About PyMC3", "history")
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],
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# "fixed_sidebar": "false",

‎docs/source/developer_guide.rst

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‎docs/source/index.rst

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

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# (Generalized) Linear and Hierarchical Linear Models in PyMC3"
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"# GLM: Linear Regression"
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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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"source": [
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"## Linear Regression\n",
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"Simple example\n",
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"==============\n",
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"\n",
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"Lets generate some data with known slope and intercept and fit a simple linear GLM."
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Robust GLM\n",
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"Robust GLM\n",
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"==========\n",
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"\n",
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"Lets try the same model but with a few outliers in the data."
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Hierarchical GLM"
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"# Hierarchical GLM"
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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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"source": [
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"## Logistic Regression"
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"# Logistic Regression"
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]
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},
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{

‎docs/source/notebooks/MvGaussianRandomWalk_demo.ipynb

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‎docs/source/notebooks/api_quickstart.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# General API quickstart"
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"# API quickstart"
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]
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},
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{

‎docs/source/notebooks/cox_model.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cox model"
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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": 1,
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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": "3.6.1"
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},
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"latex_envs": {
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"bibliofile": "biblio.bib",

‎docs/source/notebooks/gaussian_process.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Gaussian Processes using numpy kernel\n",
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"# Gaussian Processes\n",
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"\n",
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"(c) 2016 by Chris Fonnesbeck"
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]

‎docs/source/notebooks/table_of_contents_examples.js

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Gallery.contents = {
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"BEST": "Case Studies",
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"LKJ": "Case Studies",
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"dawid-skene": "Case Studies",
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"stochastic_volatility": "Case Studies",
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"rugby_analytics": "Case Studies",
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"multilevel_modeling": "Case Studies",
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"Diagnosing_biased_Inference_with_Divergences": "Diagnostics and Model Criticism",
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"model_comparison": "Diagnostics and Model Criticism",
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"posterior_predictive": "Diagnostics and Model Criticism",
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"Bayes_factor": "Diagnostics and Model Criticism",
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"GLM": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-linear": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-logistic": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-hierarchical-binominal-model": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-hierarchical": "(Generalized) Linear and Hierarchical Linear Models",
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"hierarchical_partial_pooling": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-model-selection": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-negative-binomial-regression": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-poisson-regression": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-robust-with-outlier-detection": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-robust": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-rolling-regression": "(Generalized) Linear and Hierarchical Linear Models",
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"GLM-hierarchical-advi-minibatch": "(Generalized) Linear and Hierarchical Linear Models",
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"AR": "Time Series",
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"BEST": "Applied",
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"Bayes_factor": "Other",
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"Diagnosing_biased_Inference_with_Divergences": "Diagnostics",
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"Euler-Maruyama_and_SDEs": "Time Series",
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"GLM-hierarchical-advi-minibatch": "Variational Inference",
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"GLM-hierarchical-binominal-model": "GLMs",
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"GLM-hierarchical": "GLMs",
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"GLM-linear": "GLMs",
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"GLM-logistic": "GLMs",
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"GLM-model-selection": "GLMs",
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"GLM-negative-binomial-regression": "GLMs",
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"GLM-poisson-regression": "GLMs",
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"GLM-robust-with-outlier-detection": "GLMs",
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"GLM-robust": "GLMs",
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"GLM-rolling-regression": "GLMs",
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"GLM": "GLMs",
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"GP-Kron": "Gaussian Processes",
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"GP-Latent": "Gaussian Processes",
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"GP-Marginal": "Gaussian Processes",
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"GP-TProcess": "Gaussian Processes",
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"GP-slice-sampling": "Gaussian Processes",
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"GP-smoothing": "Gaussian Processes",
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"gaussian_process": "Gaussian Processes",
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"MvGaussianRandomWalk_demo": "Time Series",
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"SMC2_gaussians": "Other",
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"bayes_param_survival_pymc3": "Survival Analysis",
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"bayesian_neural_network_advi": "Variational Inference",
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"bayesian_neural_network_with_sgfs": "Stochastic Gradients",
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"censored_data": "Survival Analysis",
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"constant_stochastic_gradient": "Stochastic Gradients",
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"convolutional_vae_keras_advi": "Variational Inference",
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"cox_model": "Other",
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"dawid-skene": "Applied",
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"dependent_density_regression": "Mixture Models",
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"dp_mix": "Mixture Models",
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"empirical-approx-overview": "Variational Inference",
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"gaussian-mixture-model-advi": "Mixture Models",
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"gaussian_mixture_model": "Mixture Models",
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"gaussian_process": "Gaussian Processes",
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"hierarchical_partial_pooling": "GLMs",
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"lda-advi-aevb": "Variational Inference",
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"marginalized_gaussian_mixture_model": "Mixture Models",
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"SMC2_gaussians": "Simulation-based Inference",
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"bayesian_neural_network_with_sgfs": "Stochastic Gradients",
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"constant_stochastic_gradient": "Stochastic Gradients",
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"model_comparison": "Diagnostics",
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"multilevel_modeling": "Applied",
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"normalizing_flows_overview": "Variational Inference",
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"posterior_predictive": "Diagnostics",
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"rugby_analytics": "Applied",
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"sgfs_simple_optimization": "Stochastic Gradients",
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"bayes_param_survival_pymc3": "Survival Analysis",
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"censored_data": "Survival Analysis",
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"stochastic_volatility": "Applied",
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"survival_analysis": "Survival Analysis",
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"weibull_aft": "Survival Analysis",
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"cox_model": "Survival Analysis",
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"MvGaussianRandomWalk_demo": "Time Series",
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"AR": "Time Series",
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"Euler-Maruyama_and_SDEs": "Time Series",
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"bayesian_neural_network_advi": "Variational Inference",
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"convolutional_vae_keras_advi": "Variational Inference",
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"empirical-approx-overview": "Variational Inference",
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"lda-advi-aevb": "Variational Inference",
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"normalizing_flows_overview": "Variational Inference",
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"gaussian-mixture-model-advi": "Variational Inference",
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"GLM-hierarchical-advi-minibatch": "Variational Inference"
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"weibull_aft": "Survival Analysis"
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}

‎docs/source/notebooks/table_of_contents_tutorials.js

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Gallery.contents = {
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"api_quickstart": "Basics",
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"variational_api_quickstart": "Basics",
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"theano": "Basics",
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"prob_dists": "Basics",
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"gp": "Basics",
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"sampling_compound_step": "Deep dives",
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"sampler-stats": "Deep dives",
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"Diagnosing_biased_Inference_with_Divergences": "Deep dives",
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"advanced_theano": "Deep dives",
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"getting_started": "Deep dives",
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"PyMC3_tips_and_heuristic": "How-To",
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"blackbox_external_likelihood": "How-To",
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"getting_started": "Basics",
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"sampler-stats": "Basics",
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"sampling_compound_step": "Basics",
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"howto_debugging": "Basics",
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"live_sample_plots": "How-To",
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"profiling": "How-To",
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"howto_debugging": "How-To",
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"model_averaging": "How-To",
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"updating_priors": "How-To",
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"live_sample_plots": "How-To",
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"lasso_block_update": "How-To"
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"lasso_block_update": "How-To",
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"model_averaging": "How-To",
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"blackbox_external_likelihood": "How-To",
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"LKJ": "How-To",
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"variational_api_quickstart": "How-To",
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"PyMC3_tips_and_heuristic": "How-To"
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}

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