Description
Issue with current documentation:
When I navigate to certain example notebooks in the gallery, the page does not correctly render. I tested on two machines (Mac/Windows) in two browsers (Firefox/Safari/Edge) after clearing the browser cache in each. Image attached below to show what I currently see for the broken examples
Here's what I get as working/not working:
Core Notebooks:
✅ Introductory Overview
❌ GLM: Linear Regression (link broken? doesn't have an class when I inspect the element)
✅ Model Comparison
✅ Prior and Posterior Predictive Checks
✅ Distribution Dimensionaltiy
✅ PyMC and PyTensor
(Generalized) Linear and Hierarchical Linear Models
❌ GLM: Model Selection
❌ GLM: Robust Linear Regression
✅ Simpson's paradox and mixed models
❌ Binomial regresion
✅ Rolling regression
❌ GLM: Robust Regression using Custom Likelihood for Outlier Classificaton
✅ Out-of-Sample Prediction
✅ GLM: Poisson Regression
✅ GLM: Negative Binomial Regression
❌ Hierarchical Binomial Model: Rat Tumor Example
❌ Bayesian regression with truncated or censored data
Case Studies
❌ Splines
✅ LKJ Cholesky Covariance Priors for Multivariate Normal Models
❌ Model building and expansion for gold putting
❌ Introduction to Bayesian A/B Testing
❌ Quantile Regression with BART
❌ NBA FOul Analysis with Item Response Theory
✅ Estimating parameters of a distribution from awkwardly binned data
✅ Factor analysis
❌ Modeling Heteroscedasticity with BART
✅ How to wrap a JAX function for use in PyMC
✅ Conditional Autoregressive (CAR) model
❌ Reliability statistics and predictive calibration
❌ Bayesian Additive Regression Trees: Introduction
❌ Probabilistic Matrix Factorization for making personalized recommendations
❌ Bayesian moderation analysis
❌ A hierarchical model for rugby prediction
❌ Bayesian Estimation Supersedes the T-Test
❌ A primer of Bayesian Methods for Multilevel modeling
✅ Using a "black box" likelihood function (numpy)
✅ Using a "black box" likelihood function (Cython)
❌ Hierarchical partial pooling
❌ Bayesian mediation analysis
❌ Fitting a reinforcement learning model to behavorial data with PyMC
❌ Stochastic Volatility model
❌ Generalized extreme value distribution
❌ Bayesian Missing data imputation
Causal Inference
❌ Counterfactual inference
❌ Difference in differences
✅ Regression discontinuity design analysis
❌ Interrupted time series analysis
Diagnostics and Model Criticism
✅ Diagnosing baised inference with divergences
✅ Sampler statistics
❌ Model averaging
❌ Bayes factors and marginal likelihoods
Gaussian Processes
❌ Multi-output guassian processes
✅ Heteroskedastic gaussian processes
✅ Marginal likelihod implementation
✅ Gaussian process for C)2 at Mauna Loa
✅ Example: Mauna Loa CO_2 continued
✅ Sparse approximations
✅ Gaussian process using numpy kernel
✅ Gaussian Process (GP) Smoothing
✅ Kronecker structured covariances
✅ GP-Circular
✅ Modeling spatial point patters with a marked log-Gaussian Cox process
❌ Mean and covariance functions
✅ Gaussian processes: Latent variable implementation
✅ Student-t process
Inference in ODE models
✅ Lotka-Volterra with manual gradients
❌ ODE Lota-Volterra with bayesian interence in multiple ways
✅ PyMC3.ode Shapes and Benchmarking
✅ GSoC 2019: Introduction of pym3.ode API
MCMC
✅ Multilevel gravity survery with MLDA
✅ Using JAX for faster sampling
✅ Sequential Monte Carlo
✅ DEMetropolis and DEMetropolis(Z) Algorithm Comparisons
✅ The MLDA sampler
❌ Approximate Bayesian Computation
✅ Variance reduction in MLDA - Linear regression
❌ DEMetropolis(Z) sampler tuning
✅ MLDA sampler: Introduction and resources
Mixture Models
✅ Dirchlet process mixtures for density estimation
✅ Dependent density regression
✅ Dirichlet mixtures of multinomials
✅ Gaussian mixture model
✅ Marginalized gaussian mixture model
Survival Analysis
✅ Censored data models
✅ Reparameterizing the Weibull Accelerated Failure time model
✅ Bayesian Survival Analysis
✅ Bayesian Parameteric Survival Analysis with PyMC3
Time Series
✅ Inferring parameters for SDEs using an Euler-Maruyama scheme
❌ Multivariate Gaussian Random Walk
❌ Bayesian Vector Autoregressive Models
✅ Forecasting with Structural AR Timeseries
✅ Analysis of an AR(1) model in PyMC
❌ Air passengers -Prophet-like model
Variational Inference
❌ Pathfinder Variational Inference
✅ GLM: Mini-batch ADVI on hierarchical regression model
✅ Introduction to Variational Inference with PymC
❌ Variational Inference: Bayesian Neural Networks
✅ Empirical Approximation overview
How to
✅ Compound steps in sampling
✅ Profiling
✅ Lasso regression with block updating
✅ Using a custom step method for sampling from locally conjugate posterior distributions
✅ Updating priors
❌ Using shared variables (Data container adaptation)
✅ How to debug a mode
❌ General API quickstart
✅ Using ModelBuilder class for deploying PyMC models
✅ Sample callback
✅ Defining a custom distribution in PyMC3
Idea or request for content:
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