PyMC Example Gallery# Core notebooks# Introductory Overview of PyMC GLM: Linear regression Model Comparison Prior and Posterior Predictive Checks Distribution Dimensionality PyMC and Aesara (Generalized) Linear and Hierarchical Linear Models# Bayesian regression with truncated or censored data Simpson’s paradox and mixed models Rolling Regression GLM: Robust Regression using Custom Likelihood for Outlier Classification GLM: Robust Linear Regression GLM: Poisson Regression GLM in PyMC3: Out-Of-Sample Predictions GLM: Negative Binomial Regression GLM: Model Selection Hierarchical Binomial Model: Rat Tumor Example GLM: Hierarchical Linear Regression Binomial regression Case Studies# How to wrap a JAX function for use in PyMC Stochastic Volatility model Splines A Hierarchical model for Rugby prediction Fitting a Reinforcement Learning Model to Behavioral Data with PyMC Model building and expansion for golf putting Probabilistic Matrix Factorization for Making Personalized Recommendations A Primer on Bayesian Methods for Multilevel Modeling Bayesian moderation analysis Bayesian mediation analysis Modeling spatial point patterns with a marked log-Gaussian Cox process NBA Foul Analysis with Item Response Theory Hierarchical Partial Pooling Factor analysis Conditional Autoregressive (CAR) model Using a “black box” likelihood function (numpy) Using a “black box” likelihood function (Cython) Estimating parameters of a distribution from awkwardly binned data Introduction to Bayesian A/B Testing LKJ Cholesky Covariance Priors for Multivariate Normal Models Bayesian Estimation Supersedes the T-Test Bayesian Additive Regression Trees: Introduction Causal Inference# Regression discontinuity design analysis Counterfactual inference: calculating excess deaths due to COVID-19 Diagnostics and Model Criticism# Sampler Statistics Model Averaging Diagnosing Biased Inference with Divergences Bayes Factors and Marginal Likelihood Gaussian Processes# Gaussian Processes using numpy kernel Gaussian Process (GP) smoothing Student-t Process Sparse Approximations Mean and Covariance Functions Example: Mauna Loa CO_2 continued Gaussian Process for CO2 at Mauna Loa Marginal Likelihood Implementation Latent Variable Implementation Kronecker Structured Covariances Heteroskedastic Gaussian Processes GP-Circular Inference in ODE models# Lotka-Volterra with manual gradients pymc3.ode: Shapes and benchmarking GSoC 2019: Introduction of pymc3.ode API MCMC# Sequential Monte Carlo Approximate Bayesian Computation Variance reduction in MLDA - Linear regression The MLDA sampler MLDA sampler: Introduction and resources Multilevel Gravity Survey with MLDA Using JAX for faster sampling DEMetropolis(Z): tune_drop_fraction DEMetropolis(Z): Population vs. History efficiency comparison Mixture Models# Marginalized Gaussian Mixture Model Gaussian Mixture Model Dirichlet process mixtures for density estimation Dirichlet mixtures of multinomials Dependent density regression Survival Analysis# Reparameterizing the Weibull Accelerated Failure Time Model Bayesian Survival Analysis Censored Data Models Bayesian Parametric Survival Analysis with PyMC3 Time Series# Multivariate Gaussian Random Walk Inferring parameters of SDEs using a Euler-Maruyama scheme Air passengers - Prophet-like model Analysis of An AR(1) Model in PyMC3 Variational Inference# Variational API quickstart Normalizing Flows Overview Automatic autoencoding variational Bayes for latent dirichlet allocation with PyMC3 Gaussian Mixture Model with ADVI Empirical Approximation overview Convolutional variational autoencoder with PyMC3 and Keras Variational Inference: Bayesian Neural Networks GLM: Mini-batch ADVI on hierarchical regression model How to# Updating priors Using a custom step method for sampling from locally conjugate posterior distributions Compound Steps in Sampling Sample callback Profiling Lasso regression with block updating How to debug a model Using shared variables (Data container adaptation) Defining a Custom Distribution in PyMC3 General API quickstart
